Verified Tech Jobs & Hiring Companies, Updated Every 24 Hours

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 role involves forming technical hypotheses, designing targeted tests, analyzing system logs and low-level hardware data, and distinguishing between GPU, host, firmware, interconnect, thermal, and physical hardware failures. It requires strong ownership, curiosity, and the ability to turn ambiguous problems into evidence-based conclusions and repeatable troubleshooting procedures. This is a senior systems engineering role focused on diagnosing and resolving complex hardware and platform failures in high-performance AI infrastructure. This engineer will work across Linux, GPU servers, PCIe, firmware, power, and cooling to determine the true root cause of issues that may not have an existing troubleshooting procedure. What skills we expect you to have: Minimum 5 years of hands-on systems engineering experience across Linux, server hardware, firmware, PCIe, and GPU or high-performance computing platforms. Extensive Linux experience, particularly using Linux as an environment for hardware debugging, system-level troubleshooting, and platform investigation. Knowledge of the Linux kernel and experience with kernel-level debugging or troubleshooting. Strong knowledge of modern server architecture, particularly in high-performance, GPU-based environments. Strong knowledge of NVIDIA GPU platforms and diagnostic tooling, including nvidia-smi, XID and SXID analysis, GSP and driver behaviour, NVLink, NVSwitch, Fabric Manager, DCGM diagnostics, and NCCL testing Deep knowledge of PCIe, including topology, enumeration, root complexes, endpoints, switches, bridges, retimers, link speed and width, AER and DPC errors, completion timeouts, and link failures. Candidates should understand how protocol-level errors can relate to physical-layer or signal-integrity problems. Experience benchmarking systems for performance, stability, power efficiency, thermal behaviour, and workload characteristics In depth understanding of firmware interactions across BIOS/UEFI, BMC, CPLD/FPGA, GPU firmware, NIC firmware, and other platform components. Experience with low-level hardware communication and debugging using I²C, SMBus, PMBus, register maps, bit masks, byte- and word-level data, and device datasheets. Demonstrated ability to troubleshoot complex hardware, software, and networking issues. Experience with deep problem investigation, root cause analysis, and performance optimization in cloud or high-performance computing environments. Strong analytical and problem-solving skills with a performance-first mindset. Scripting and automation using various languages (Python\Go) 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. Compensation We offer a competitive salary ranging from $179,500- $224,300 + quarterly performance bonuses. Join Nebius Today! 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. We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search. Your responsibilities: Design, train, and deploy ML models for retrieval, reranking, and search relevance in production Build and optimise embedding-based indexing and large-scale retrieval systems Develop models supporting crawling, data selection, and content understanding Define and improve quality metrics for agent-native search and build evaluation pipelines Work on systems operating at very large scale, including high-throughput query workloads Collaborate closely with engineering teams to integrate ML models into production services Analyse performance trade-offs across latency, quality, and cost Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems Contribute to product and architectural decisions in a fast-moving environment Must-haves: 5+ years of experience in software engineering or applied machine learning Strong programming skills in Python, Go, or C++ Proven experience deploying ML models in production systems Hands-on experience with retrieval, ranking, recommendation, or similar ML problems Strong understanding of machine learning and modern deep learning techniques Experience working with large-scale data systems and high-throughput environments Ability to design evaluation frameworks and define meaningful model metrics Product-oriented mindset with a focus on impact and iteration Strong problem-solving skills and ability to work in a distributed team Nice-to-haves: Experience with search systems or large-scale information retrieval Familiarity with embeddings, transformers, and modern NLP systems Experience working on LLM-powered or agent-based systems Contributions to open-source projects, technical publications, or conference talks Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability We conduct coding interviews as part of the process. 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. We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search. Your responsibilities: Design, train, and deploy ML models for retrieval, reranking, and search relevance in production Build and optimise embedding-based indexing and large-scale retrieval systems Develop models supporting crawling, data selection, and content understanding Define and improve quality metrics for agent-native search and build evaluation pipelines Work on systems operating at very large scale, including high-throughput query workloads Collaborate closely with engineering teams to integrate ML models into production services Analyse performance trade-offs across latency, quality, and cost Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems Contribute to product and architectural decisions in a fast-moving environment Must-haves: 5+ years of experience in software engineering or applied machine learning Strong programming skills in Python, Go, or C++ Proven experience deploying ML models in production systems Hands-on experience with retrieval, ranking, recommendation, or similar ML problems Strong understanding of machine learning and modern deep learning techniques Experience working with large-scale data systems and high-throughput environments Ability to design evaluation frameworks and define meaningful model metrics Product-oriented mindset with a focus on impact and iteration Strong problem-solving skills and ability to work in a distributed team Nice-to-haves: Experience with search systems or large-scale information retrieval Familiarity with embeddings, transformers, and modern NLP systems Experience working on LLM-powered or agent-based systems Contributions to open-source projects, technical publications, or conference talks Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability We conduct coding interviews as part of the process. 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. We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search. Your responsibilities: Design, train, and deploy ML models for retrieval, reranking, and search relevance in production Build and optimise embedding-based indexing and large-scale retrieval systems Develop models supporting crawling, data selection, and content understanding Define and improve quality metrics for agent-native search and build evaluation pipelines Work on systems operating at very large scale, including high-throughput query workloads Collaborate closely with engineering teams to integrate ML models into production services Analyse performance trade-offs across latency, quality, and cost Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems Contribute to product and architectural decisions in a fast-moving environment Must-haves: 5+ years of experience in software engineering or applied machine learning Strong programming skills in Python, Go, or C++ Proven experience deploying ML models in production systems Hands-on experience with retrieval, ranking, recommendation, or similar ML problems Strong understanding of machine learning and modern deep learning techniques Experience working with large-scale data systems and high-throughput environments Ability to design evaluation frameworks and define meaningful model metrics Product-oriented mindset with a focus on impact and iteration Strong problem-solving skills and ability to work in a distributed team Nice-to-haves: Experience with search systems or large-scale information retrieval Familiarity with embeddings, transformers, and modern NLP systems Experience working on LLM-powered or agent-based systems Contributions to open-source projects, technical publications, or conference talks Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability We conduct coding interviews as part of the process. 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. We are seeking a Senior Applied ML Engineer to join a fast-growing team building an agent-native search platform for AI systems, the emerging web access layer for AI. You will develop and deploy machine learning models that power retrieval, ranking, and indexing at scale, helping AI systems access fresh, reliable information in real time. This is a high-impact role working on a production system used 24x7, tackling challenges comparable to large-scale web search. Your responsibilities: Design, train, and deploy ML models for retrieval, reranking, and search relevance in production Build and optimise embedding-based indexing and large-scale retrieval systems Develop models supporting crawling, data selection, and content understanding Define and improve quality metrics for agent-native search and build evaluation pipelines Work on systems operating at very large scale, including high-throughput query workloads Collaborate closely with engineering teams to integrate ML models into production services Analyse performance trade-offs across latency, quality, and cost Experiment with and apply state-of-the-art techniques in search, retrieval, and LLM-integrated systems Contribute to product and architectural decisions in a fast-moving environment Must-haves: 5+ years of experience in software engineering or applied machine learning Strong programming skills in Python, Go, or C++ Proven experience deploying ML models in production systems Hands-on experience with retrieval, ranking, recommendation, or similar ML problems Strong understanding of machine learning and modern deep learning techniques Experience working with large-scale data systems and high-throughput environments Ability to design evaluation frameworks and define meaningful model metrics Product-oriented mindset with a focus on impact and iteration Strong problem-solving skills and ability to work in a distributed team Nice-to-haves: Experience with search systems or large-scale information retrieval Familiarity with embeddings, transformers, and modern NLP systems Experience working on LLM-powered or agent-based systems Contributions to open-source projects, technical publications, or conference talks Participation in competitive ML (e.g. Kaggle) or similar signals of strong technical ability We conduct coding interviews as part of the process. 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 Token Factory is a part of Nebius Cloud, one of the world’s largest GPU clouds, running tens of thousands of GPUs. We are building an inference & fine-tuning platform that makes every kind of foundation model — text, vision, audio, and emerging multimodal architectures — fast, reliable, and effortless to train & deploy at massive scale. Some directions we currently working on and which you can be a part of: Advanced Fine-Tuning: Enhancing fine-tuning methodologies - both LoRA-based and full-parameter - for cutting-edge LLMs (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-4.7), focusing on both model quality and training efficiency. Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. This involves building model training and evaluation pipelines in JAX for speculative decoding, experimenting with architectures (dense/MoE, auto-regressive/parallel), and deriving scaling laws to guide resource allocation. Low Precision Training & Inference: Investigating low-precision (FP8, NVFP4/MXFP4) methodologies for supervised fine-tuning and reinforcement learning - spanning both inference and training - optimized for modern hardware We expect you to have: A profound understanding of theoretical foundations of machine learning and reinforcement learning. Deep expertise in modern deep learning for language processing and generation Experience with training large models on multiple computational nodes Reasonable understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.) Strong software engineering skills (we mostly use Python) Deep experience with modern deep learning frameworks (we use JAX) Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing Strong communication and leadership abilities Nice to have: Previous experience working with language models or other similar NLP technologies. Familiarity with important ideas in LLM space, such as MHA, RoPE, ZeRO/FSDP, Flash Attention, quantization A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment. Strong engineering skills, including experience in developing large distributed systems or high-load web services. Open-source projects that showcase your engineering prowess Excellent command of the English language, alongside superior writing, articulation, and communication skills. 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.