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About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory needs scientists who can turn frontier inference bottlenecks into research problems, publish credible work, and then help ship the results into production. This is not a papers-only research role. The Applied Scientist is expected to design rigorous experiments, write strong code, collaborate with engineers, and convert research into deployed inference capabilities. A Senior Applied Scientist owns well-scoped research and production optimization projects. They can publish or prepare high-quality technical work while also producing code, experiments, and prototypes that engineers can use. Your responsibilities : Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff. Prepare internal reports, technical blogs, or papers when the work is externally credible. Partner directly with MLEs to ensure research prototypes become usable production components. Define and execute research programs in efficient LLM and VLM inference with measurable production impact. Invent, evaluate, and productionize methods for quantization, QAT , distillation, speculative decoding, KV -cache reuse, KV -cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build high-quality prototypes in PyTorch, Triton, CUDA -adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them. Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for Nebius Token Factory. Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets. Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs. Must-haves : PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field. Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong hands-on coding ability in Python and PyTorch; ability to move from idea to experiment to prototype quickly. Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs. Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice - to - have s : First-author publications in NeurIPS, ICML , ICLR , MLSys, ACL , EMNLP , ASPLOS , OSDI , SOSP , ISCA , HPCA , or comparable venues. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA , or PyTorch internals. Experience with post-training, SFT , DPO , RLHF , RLAIF , preference optimization, or synthetic data generation when connected to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory needs scientists who can turn frontier inference bottlenecks into research problems, publish credible work, and then help ship the results into production. This is not a papers-only research role. The Applied Scientist is expected to design rigorous experiments, write strong code, collaborate with engineers, and convert research into deployed inference capabilities. A Senior Applied Scientist owns well-scoped research and production optimization projects. They can publish or prepare high-quality technical work while also producing code, experiments, and prototypes that engineers can use. Your responsibilities : Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff. Prepare internal reports, technical blogs, or papers when the work is externally credible. Partner directly with MLEs to ensure research prototypes become usable production components. Define and execute research programs in efficient LLM and VLM inference with measurable production impact. Invent, evaluate, and productionize methods for quantization, QAT , distillation, speculative decoding, KV -cache reuse, KV -cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build high-quality prototypes in PyTorch, Triton, CUDA -adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them. Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for Nebius Token Factory. Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets. Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs. Must-haves : PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field. Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong hands-on coding ability in Python and PyTorch; ability to move from idea to experiment to prototype quickly. Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs. Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice - to - have s : First-author publications in NeurIPS, ICML , ICLR , MLSys, ACL , EMNLP , ASPLOS , OSDI , SOSP , ISCA , HPCA , or comparable venues. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA , or PyTorch internals. Experience with post-training, SFT , DPO , RLHF , RLAIF , preference optimization, or synthetic data generation when connected to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory needs scientists who can turn frontier inference bottlenecks into research problems, publish credible work, and then help ship the results into production. This is not a papers-only research role. The Applied Scientist is expected to design rigorous experiments, write strong code, collaborate with engineers, and convert research into deployed inference capabilities. A Senior Applied Scientist owns well-scoped research and production optimization projects. They can publish or prepare high-quality technical work while also producing code, experiments, and prototypes that engineers can use. Your responsibilities : Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff. Prepare internal reports, technical blogs, or papers when the work is externally credible. Partner directly with MLEs to ensure research prototypes become usable production components. Define and execute research programs in efficient LLM and VLM inference with measurable production impact. Invent, evaluate, and productionize methods for quantization, QAT , distillation, speculative decoding, KV -cache reuse, KV -cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build high-quality prototypes in PyTorch, Triton, CUDA -adjacent tooling, or inference-serving frameworks, then work with MLEs and platform engineers to productionize them. Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Publish papers, technical reports, blog posts, and open-source artifacts that build external credibility for Nebius Token Factory. Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets. Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs. Must-haves : PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field. Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong hands-on coding ability in Python and PyTorch; ability to move from idea to experiment to prototype quickly. Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs. Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice - to - have s : First-author publications in NeurIPS, ICML , ICLR , MLSys, ACL , EMNLP , ASPLOS , OSDI , SOSP , ISCA , HPCA , or comparable venues. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA , or PyTorch internals. Experience with post-training, SFT , DPO , RLHF , RLAIF , preference optimization, or synthetic data generation when connected to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role The Field CTO, Media & Entertainment AI Infrastructure is a senior engineering leader who will initially operate as an individual contributor at the intersection of deep AI infrastructure engineering and the business of media and entertainment, with a clear path to building and leading a team as the business scales. You will work alongside CTOs and engineering leaders at studio groups, VFX houses, gaming studios, agency holding companies, and generative AI model companies. Your role is not to pitch them; it is to build with them. You will translate their most complex infrastructure problems into engineered solutions, own the technical relationship with our most strategic ISV and cloud-native partners, and use what you learn in the field to directly influence the M&E product roadmap alongside Nebius's global Head of Product and Head of Engineering. This is a ground-floor opportunity to define what AI-native infrastructure looks like for an industry in the middle of a fundamental transformation. You are welcome to work remotely from the United States (SF Bay Area or NYC preferred) Travel expectations 10% - 20% for executive meetings and key industry events. Your responsibilities will include : AI Infrastructure Architecture Own the Technical Blueprint: Personally architect the infrastructure solutions for our most strategic M&E partnerships , studio-scale content production pipelines, agency data consolidation plays, generative AI model deployments. These architectures must be engineered to survive real-world scale, not just pass a POC. The Physics to P&L Narrative: Fluently demonstrate to executive stakeholders how infrastructure decisions , data lake locality, storage tiering, inference optimization, directly impact their business model and operability. Forensic Requirement Gathering Deconstruct the Bottleneck: Go beyond the stated problem to find the technical truth. Translate vague business goals (e.g., “We need lower rendering costs”) into precise engineering requirements (e.g., “We need to optimize the inference batch size on L40s to reduce cost-per-token by 30%”). Map the Transition: Identify exactly where a customer sits on the curve from legacy service bureau to AI-native tech platform and prescribe the specific infrastructure intervention needed to move them forward. ISV & Partner Technical Strategy Build and Validate the Integration Layer: Identify, engage, and technically validate relationships with the most critical ISVs in the media and entertainment landscape, from rendering and VFX toolchains to generative AI platforms. Define the Standard: It is not enough to support these tools. You will define the reference architectures for how they run best on Nebius infrastructure, and work directly with ISV engineering teams to build and publish those standards. Decide What’s Worth Doing: In partnership with the GM, evaluate ISV and partner opportunities on their technical merit and strategic leverage , and be equally rigorous about what not to pursue. Internal Technical Influence Shape the M&E Roadmap: Use forensic evidence from the field to prioritize and justify the M&E vertical roadmap. You will work directly with Nebius’s global Head of Product and Head of Engineering to translate partner and customer needs into product direction. Lead the M&E Product Summit: Chair a quarterly summit with Core Engineering leadership, using field evidence to drive roadmap decisions and maintain vertical momentum. We expect you to have : 12+ years of experience in cloud infrastructure, platform engineering, distributed systems, or a closely related technical domain. Executive Presence: Capable of commanding a room of engineers and presenting a layered technical roadmap to a C-Suite. You have operated at the top-to-top level , your counterparts are CTOs and VPs of Engineering. Builder Mentality: This role begins as a hands-on individual contributor position. You will architect and ship solutions alongside partners and build the assets (reference architectures, integration playbooks, technical frameworks) that make Nebius's M&E infrastructure strategy defensible and scalable. As the business case develops, this role is explicitly designed to grow into a team-building and leadership function, with the expectation that you will recruit, shape, and run that team. You may have come from a startup, run your own company, or operated within a large org in a way that felt like building from zero, and you know how to transition from maker to multiplier. Product-Minded: Experience defining a platform strategy, not just executing tickets. You are comfortable telling a customer “No” when a request creates technical debt, and proposing a better alternative. Ambiguity Tolerance: You thrive in environments where requirements are evolving. You do not wait for a roadmap; you build it. Forensic Mindset: You are not satisfied with surface-level answers. You dig into the kernel, the logs, and the P&L to find the truth. AI Infrastructure Expertise Mastery of the Stack: Expert-level, production-grade knowledge of GPU architectures (H100, L40s), Kubernetes orchestration including Soperator, high-performance and parallel file systems (e.g., Lustre, WEKA), data lake architecture, and networking constraints (InfiniBand/Ethernet). Inference Optimization: You understand the nuances of model serving , batch sizes, quantization, KV caching, latency tradeoffs , and can architect solutions for both massive throughput and real-time (sub-50ms) demands. Domain Context: Media & Entertainment Industry Fluency: You have operated within the M&E ecosystem and can speak to the infrastructure implications across the following sub-sectors: Gaming Multimodal Generative Models AdTech VFX / Content Production Non-negotiable: Candidates without deep, production-grade working knowledge of GPU infrastructure and inference-based solutioning will not be considered. This is a hard requirement, not a preference. AI Infrastructure Expertise Mastery of the Stack: You must be an expert in the physics of AI infrastructure. This includes deep, production-grade knowledge of GPU architectures (H100 vs. L40s), Kubernetes orchestration (K8s), high-performance storage parallel file systems (e.g., Lustre, WEKA), and networking constraints (InfiniBand/Ethernet). Inference Optimization: You understand the nuances of model serving—batch sizes, quantization, KV caching, and latency tradeoffs—and can architect solutions for both massive throughput and real-time (sub-50ms) demands. It will be an added bonus if you have : Hands-on experience with VFX pipelines, studio-scale content production workflows, or the cloud tooling that powers them , render farm management, distributed simulation, asset pipeline tooling. You understand how content gets made, moved, and transformed at scale, and what infrastructure makes that possible. Strategic Empathy: The ability to distinguish between customers who need raw bare-metal access and those who need managed endpoints, and the wisdom to prescribe the right path. Key Employee Benefits : Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) Plan: Up to 4% company match with immediate vesting. Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote Work Reimbursement: Up to $85/month for mobile and internet. Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $200,000 — $245,000 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Your responsibilities will include: Develop and maintain Python-based API integrations between our HR information system (HRIS) and business systems, including finance, travel, expense management, and recruiting platforms. Build and maintain cloud-native services, web applications, and containerized workloads on Microsoft Azure. Implement automation for system workflows, onboarding/offboarding, access-related processes, and data synchronization. Create and maintain deployment pipelines and Infrastructure-as-Code with Terraform. Own integrations through implementation, testing, deployment, and production support; respond to alerts and troubleshoot issues across APIs, Azure services, and data sources within agreed SLAs. Implement safeguards for sensitive employee data, including validation, audit trails, and controls around mass changes. Work with business stakeholders to clarify requirements and maintain technical documentation as part of each change. We expect you to have: 3+ years of integration or backend development experience, including hands-on experience deploying and supporting production workloads in Microsoft Azure. Practical experience with Azure compute and integration services. Our stack includes Azure Functions, App Service, Container Apps, Container Instances, Service Bus, and API Management. Practical Python development skills, including maintaining and debugging existing services and automations. Experience integrating REST APIs, webhooks, and Microsoft Graph API, with an understanding of pagination, rate limits, retries, and idempotency. Experience working with CI/CD pipelines and Terraform for Azure deployments. Good understanding of OAuth 2.0, Microsoft Entra ID, and managed identities for secure authentication and service-to-service access. Experience designing and working with relational databases (PostgreSQL, MSSQL, etc.). Experience with automated testing and diagnosing production issues through logs, monitoring, and alerts. It will be an added bonus if you have: Experience integrating HRIS or other business systems, such as ERP, expense management, or recruiting platforms. Azure certifications (AZ-204, AZ-900, AZ-104). Hands-on experience with Application Insights and Log Analytics. Experience using AI-assisted development tools, with the ability to critically evaluate their output. #LI-RK1 Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role The Field CTO, Media & Entertainment AI Infrastructure is a senior engineering leader who will initially operate as an individual contributor at the intersection of deep AI infrastructure engineering and the business of media and entertainment, with a clear path to building and leading a team as the business scales. You will work alongside CTOs and engineering leaders at studio groups, VFX houses, gaming studios, agency holding companies, and generative AI model companies. Your role is not to pitch them; it is to build with them. You will translate their most complex infrastructure problems into engineered solutions, own the technical relationship with our most strategic ISV and cloud-native partners, and use what you learn in the field to directly influence the M&E product roadmap alongside Nebius's global Head of Product and Head of Engineering. This is a ground-floor opportunity to define what AI-native infrastructure looks like for an industry in the middle of a fundamental transformation. You are welcome to work remotely from the United States (SF Bay Area or NYC preferred) Travel expectations 10% - 20% for executive meetings and key industry events. Your responsibilities will include : AI Infrastructure Architecture Own the Technical Blueprint: Personally architect the infrastructure solutions for our most strategic M&E partnerships , studio-scale content production pipelines, agency data consolidation plays, generative AI model deployments. These architectures must be engineered to survive real-world scale, not just pass a POC. The Physics to P&L Narrative: Fluently demonstrate to executive stakeholders how infrastructure decisions , data lake locality, storage tiering, inference optimization, directly impact their business model and operability. Forensic Requirement Gathering Deconstruct the Bottleneck: Go beyond the stated problem to find the technical truth. Translate vague business goals (e.g., “We need lower rendering costs”) into precise engineering requirements (e.g., “We need to optimize the inference batch size on L40s to reduce cost-per-token by 30%”). Map the Transition: Identify exactly where a customer sits on the curve from legacy service bureau to AI-native tech platform and prescribe the specific infrastructure intervention needed to move them forward. ISV & Partner Technical Strategy Build and Validate the Integration Layer: Identify, engage, and technically validate relationships with the most critical ISVs in the media and entertainment landscape, from rendering and VFX toolchains to generative AI platforms. Define the Standard: It is not enough to support these tools. You will define the reference architectures for how they run best on Nebius infrastructure, and work directly with ISV engineering teams to build and publish those standards. Decide What’s Worth Doing: In partnership with the GM, evaluate ISV and partner opportunities on their technical merit and strategic leverage , and be equally rigorous about what not to pursue. Internal Technical Influence Shape the M&E Roadmap: Use forensic evidence from the field to prioritize and justify the M&E vertical roadmap. You will work directly with Nebius’s global Head of Product and Head of Engineering to translate partner and customer needs into product direction. Lead the M&E Product Summit: Chair a quarterly summit with Core Engineering leadership, using field evidence to drive roadmap decisions and maintain vertical momentum. We expect you to have : 12+ years of experience in cloud infrastructure, platform engineering, distributed systems, or a closely related technical domain. Executive Presence: Capable of commanding a room of engineers and presenting a layered technical roadmap to a C-Suite. You have operated at the top-to-top level , your counterparts are CTOs and VPs of Engineering. Builder Mentality: This role begins as a hands-on individual contributor position. You will architect and ship solutions alongside partners and build the assets (reference architectures, integration playbooks, technical frameworks) that make Nebius's M&E infrastructure strategy defensible and scalable. As the business case develops, this role is explicitly designed to grow into a team-building and leadership function, with the expectation that you will recruit, shape, and run that team. You may have come from a startup, run your own company, or operated within a large org in a way that felt like building from zero, and you know how to transition from maker to multiplier. Product-Minded: Experience defining a platform strategy, not just executing tickets. You are comfortable telling a customer “No” when a request creates technical debt, and proposing a better alternative. Ambiguity Tolerance: You thrive in environments where requirements are evolving. You do not wait for a roadmap; you build it. Forensic Mindset: You are not satisfied with surface-level answers. You dig into the kernel, the logs, and the P&L to find the truth. AI Infrastructure Expertise Mastery of the Stack: Expert-level, production-grade knowledge of GPU architectures (H100, L40s), Kubernetes orchestration including Soperator, high-performance and parallel file systems (e.g., Lustre, WEKA), data lake architecture, and networking constraints (InfiniBand/Ethernet). Inference Optimization: You understand the nuances of model serving , batch sizes, quantization, KV caching, latency tradeoffs , and can architect solutions for both massive throughput and real-time (sub-50ms) demands. Domain Context: Media & Entertainment Industry Fluency: You have operated within the M&E ecosystem and can speak to the infrastructure implications across the following sub-sectors: Gaming Multimodal Generative Models AdTech VFX / Content Production Non-negotiable: Candidates without deep, production-grade working knowledge of GPU infrastructure and inference-based solutioning will not be considered. This is a hard requirement, not a preference. AI Infrastructure Expertise Mastery of the Stack: You must be an expert in the physics of AI infrastructure. This includes deep, production-grade knowledge of GPU architectures (H100 vs. L40s), Kubernetes orchestration (K8s), high-performance storage parallel file systems (e.g., Lustre, WEKA), and networking constraints (InfiniBand/Ethernet). Inference Optimization: You understand the nuances of model serving—batch sizes, quantization, KV caching, and latency tradeoffs—and can architect solutions for both massive throughput and real-time (sub-50ms) demands. It will be an added bonus if you have : Hands-on experience with VFX pipelines, studio-scale content production workflows, or the cloud tooling that powers them , render farm management, distributed simulation, asset pipeline tooling. You understand how content gets made, moved, and transformed at scale, and what infrastructure makes that possible. Strategic Empathy: The ability to distinguish between customers who need raw bare-metal access and those who need managed endpoints, and the wisdom to prescribe the right path. Key Employee Benefits : Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) Plan: Up to 4% company match with immediate vesting. Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote Work Reimbursement: Up to $85/month for mobile and internet. Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $200,000 — $245,000 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role: Network Engineering Manager APAC / Network Team Lead APAC We are looking for a Staff Network Engineer / Player-Coach Team Lead to lead the growth, deployment, and operational execution of our APAC Network Infrastructure emerging team . In this role, you will combine direct people leadership with high-end technical expertise. You will lead a growing regional team of 2–5 jun/mid-to-senior network engineers , driving their professional development, cross-project prioritization across our Global Network , and overall execution aligned with global business objectives, with a priority on the APAC Region and backbone network development across EU and the US for APAC. As a hands-on technical manager, you will remain deeply embedded in engineering execution, dedicating approximately 50–70% of your time to hands-on engineering during your first year, with the role evolving naturally alongside organizational scale and regional team expansion. Crucially, this position demands a high degree of operational autonomy . Due to the timezone differences, you will serve as the primary regional networking authority, bridging global network architecture defined with our EU/EMEA/the US engineering headquarters and regional execution across APAC colocation sites, cable landings, and data centers. This position is remote within the APAC region (with preferred hubs in India, Singapore), with regular visits to regional DC facilities and our European headquarters in Amsterdam. Your responsibilities will include: Team Leadership & People Management Deploy APAC network part and BackBone. Lead, mentor, and structurally develop a regional engineering team of 2–5 jun/mid-to-senior network engineers across the APAC. Own regional task planning, backlog prioritization, change review governance, and end-to-end execution within the team. Drive and owning the Launch and Deploy process of New DataCentre and Customer in it Drive technical coaching, and personalized career roadmaps for team members. Foster a disciplined culture of radical ownership, engineering excellence, comprehensive runbook documentation, and Git-driven automation. Act as the regional net escalation point for production network incidents Global Follow-the-Sun Alignment: Co-own operational hand-off workflows and shared incident coverage with EMEA (HQ in Amsterdam) and the US network teams to guarantee seamless, round-the-clock global production network stability and customer workability. Cross-Functional Alignment & Strategic Autonomy Serve as the primary regional network owner for the next teams in APAC: datacenter operations team, site expansion teams. EU/EMEA Coordination: Actively partner with the Global Network Architecture and R&D to adapt core architectural standards (Clos fabrics, SRv6, backbone routing policies) to APAC market realities and carrier ecosystems. Autonomously drive regional connectivity delivery: partner closely with Technical Program Managers (TPMs) on submarine cable systems, cross-border DCI circuits, local Internet Exchanges (IXs), and regional transit providers. Coordinate with HWaaS, Compute, and Cloud Platform engineering teams to guarantee timely site bring-up, Day-0 Out-of-Band (OOB) readiness, and high-throughput fabric availability for production workloads. Bridge the gap between global strategic roadmaps and autonomous local incident resolution, ensuring APAC operations execute reliably during EMEA off-hours. Technical Leadership & Hands-on Work (50–60%) Full Regional Network Ownership: Own and guarantee overall network infrastructure readiness, capacity, and availability across emerging APAC network infrastructure, ensuring alignment with HQ blueprints and processes Actively contribute to the design, deployment, and operation of massive data center fabrics and backbone infrastructure. Support and participate in the evolution high-performance Ethernet-based GPU cluster interconnects. Participate in complex, high-severity troubleshooting and root-cause analysis (RCA) for critical infrastructure incidents. Oversee and contribute to network automation pipelines, tooling, and telemetry/observability development. We expect you to have: Expert-Level Technical Background: Service Provider or/and Data Center Clos networks. BGP, IS-IS, Segment Routing (SR-MPLS / SRv6), and advanced traffic balancing Ethernet switching, EVPN-VXLAN architectures, and L3 VPNs. Leadership Experience: Proven track record as a Tech Lead, Lead/Staff Engineer, or People Manager leading mid-to-senior engineering teams. Vendor Ecosystem: Juniper, Arista, Cisco, NVIDIA It will be an added bonus if you have: Hands-on experience with GPU cluster, RoCEv2/ECN or InfiniBand networks Solid understanding of Public Cloud networking models and Software-Defined Networking (SDN) overlays. Proficiency in Python or Go for infrastructure automation and production tooling within Linux environments. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The Product In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible. We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale. Behind every search request is a rich stream of signals — query patterns, retrieval decisions, crawling outcomes, ranking quality, usage, and revenue events. Turning that stream into a trustworthy, queryable data platform is what makes the product improvable, the business measurable, and the models trainable. The Role We are looking for a Data Engineer to help build and scale the data platform behind our search quality, ML pipelines, product analytics, and business operations. In this role, you will contribute across the full data lifecycle: ingesting data from production systems, designing and evolving our data warehouse, building batch and streaming pipelines, and making high-quality datasets available to researchers, engineers, analysts, and product teams across the company. The platform spans tens of terabytes and ingests data from tens of proprietary and third-party sources — including our search engine and its components, CRM, billing, identity, and product analytics across multi-region production environments. Around 100 internal users rely on it daily. You will work closely with engineers and stakeholders across the company, contribute to architectural and modeling decisions, and help improve the reliability, usability, and scalability of the data platform as it grows. In this position, your responsibility will be to: Contribute to the design, development, and operation of Tavily's data platform — from real-time ingestion through data warehouse medallion layers to consumer-facing datasets and dashboards. Build and maintain reliable batch and streaming pipelines that ingest data from production services and external systems. Design and evolve scalable, analytics-ready data models in the data warehouse. Work closely with engineers across the company to ensure data produced by production systems is reliable, well-structured, and usable downstream. Improve observability across the data platform, including data quality checks, freshness monitoring, lineage, schema evolution, and cost controls. Partner with researchers, engineers, analysts, finance, and product managers to deliver trustworthy datasets for product, search quality, ML, and GTM analytics. Contribute to defining the objects, entities, and relationships that represent Tavily's search domain — including agent inputs, URLs, chunks, agent sessions, crawls, and the connections between them — and translate them into clean, queryable data models. Improve engineering practices around testing, documentation, deployment, and incident response. Investigate and resolve production data issues, including broken pipelines, corrupted datasets, schema changes, and large-scale backfills. Contribute to technical standards and best practices for data engineering across the company. Help maintain high standards of data quality, integrity, security, and governance across environments. You may be a good fit if you: Have 5+ years of Data Engineering experience, with strong experience designing and implementing scalable, analytics-ready data models and cloud data warehouses such as Snowflake or BigQuery. Have hands-on experience with Snowflake, or a comparable cloud data warehouse, and a strong understanding of modern data warehouse architecture, preferably including medallion-style modeling. Have deep knowledge of databases, including schema design, query optimization, and familiarity with NoSQL use cases. Have strong experience with modern data orchestration and transformation frameworks such as Airflow and dbt. Understand cloud data services on AWS or GCP and have experience with streaming platforms such as Kafka or Pub/Sub. Have hands-on experience with Spark, MapReduce, or similar distributed processing systems, and understand when distributed processing is the right tool. Are fluent in Python and SQL for production data work. Have operated data systems in production: debugged them under pressure, recovered from data incidents, handled schema changes, and backfilled corrupted or incomplete datasets. Care deeply about data quality and about making datasets understandable and trustworthy for the people using them. Are comfortable working on ambiguous, cross-functional data problems and collaborating closely with both technical and non-technical stakeholders. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.