Careers at Grab Holdings

Get to Know the Team The Growth Data Science team is a key component of our regional growth strategy. We focus on creating and implementing innovative approaches to drive growth through performance marketing automation, machine learning models, geospatial insights, user lifecycle and funnel optimisation, behavioural forecasting, and other advanced analytics methods. Our work spans across transportation, food, fintech, logistics, and platform services. Get to Know the Role We are seeking a experienced and versatile Manager, Growth Data Science - Digital Marketing with deep expertise in paid marketing measurement, AI-driven analytics, and operations to join our growing team. In this role, you'll collaborate with marketing leaders, analytics teams, and cross-functional stakeholders to solve complex digital marketing challenges using advanced measurement science, artificial intelligence, and data-driven approaches. You'll work on end-to-end paid marketing analytics initiatives, with opportunities to design and implement sophisticated attribution models, incrementality studies, media mix modeling (MMM), causal inference methodologies, and AI-powered tools that enhance reporting, measurement, and experimentation capabilities. You will directly report to the Senior Manager, you will be based working onsite. The Critical Tasks You Will Perform Paid Marketing Measurement & Analytics Partner with marketing leaders and stakeholders to understand business objectives, marketing channels, data sources, measurement constraints, and strategic priorities. Translate marketing needs into relevant measurement science solutions, evaluating multiple methodological approaches and communicating trade-offs between attribution models, incrementality testing, and causal inference methods. Design and implement advanced attribution methodologies (multi-touch, algorithmic, and rule-based models) to accurately measure campaign contribution across paid channels and customer touchpoints. Develop and execute incrementality measurement studies to isolate true campaign impact, including A/B testing, holdout analysis, and matched market approaches for paid marketing campaigns. Build and maintain Media Mix Modeling (MMM) frameworks to quantify the impact of paid marketing spend across channels, optimize budget allocation, and forecast campaign performance. Design and conduct geo-lift studies to measure causal impact of paid marketing initiatives at regional or market levels, supporting strategic decision-making and ROI validation. Conduct rigorous A/B tests and multivariate tests to improve paid campaign performance, measure incremental lift, and increase conversion rates and customer acquisition efficiency. Collaborate with stakeholders to align on measurement methodology, success metrics, deliverables, and project roadmaps for all paid marketing analytics projects. AI & Automation for Marketing Intelligence Leverage advanced AI and machine learning tools to automate reporting workflows, generate real-time marketing insights, and accelerate decision-making across paid marketing channels. Design and build AI-powered measurement and attribution tools that enhance the speed and accuracy of campaign performance analysis, reducing manual effort and improving stakeholder accessibility to insights. Implement machine learning models and algorithms to optimize campaign targeting, budget allocation, and bid strategies, translating AI predictions into actionable marketing recommendations. Develop AI-driven experimentation frameworks that automate test design, statistical analysis, and result interpretation, enabling faster iteration and more sophisticated measurement of marketing impact. Data Engineering & Operations Develop and manage detailed project plans including milestones, risks, owners, and contingency plans for measurement science projects. Create and maintain efficient data pipelines using SQL, Spark, and cloud-based big data technologies. These pipelines ingest, process, and integrate paid marketing data from ad platforms, conversion tracking systems, and internal data sources. Collect, clean, and integrate large datasets from multiple marketing channels (paid search, social, display, video) and attribution platforms to support measurement requirements. Build analytics tools and dashboards that deliver applicable insights on campaign performance, channel effectiveness, customer acquisition costs, and marketing ROI. Perform exploratory data analysis, statistical modeling, and causal inference analysis to uncover insights and inform strategic marketing decisions. Train, validate, and tune measurement models using modern statistical and machine learning techniques, ensuring model accuracy and business applicability. Document measurement methodologies, model results, and findings in clear, team member-ready formats and support implementation of insights within marketing operations. Stakeholder Collaboration & Leadership Lead cross-functional collaboration between marketing, analytics, product, and data engineering teams to ensure measurement frameworks align with business objectives. Communicate complex measurement science concepts, AI capabilities, and statistical findings to non-technical stakeholders, translating results into applicable marketing recommendations. Mentor junior analysts and data scientists on measurement methodologies, AI-driven analytics best practices, paid marketing analytics, and causal inference techniques. W What Essential Skills You Will Need 5+ years of hands-on experience in data science and analytics, with at least 3+ years specifically focused on paid marketing measurement and operations Demonstrated expertise in attribution modeling methodologies (multi-touch, algorithmic, rule-based approaches) Proven experience designing and executing incrementality studies, A/B tests, and holdout analyses for paid marketing campaigns Strong background in Media Mix Modeling (MMM) or marketing mix optimization Experience designing and conducting geo-lift studies or other geographically-based causal inference studies Familiarity with causal Hands-on experience building and deploying machine learning models for marketing applications, including model optimization and performance tuning Proficiency in Python, SQL, and tools like Pandas, Scikit-learn, and Spark Working knowledge of cloud data platforms (e.g., AWS S3, Redshift) for managing large-scale marketing datasets Experience with marketing data sources and ad platform APIs (Google Ads, Facebook Ads, etc.) Manage data pipelines and ETL processes with a solid understanding of data engineering best practices Familiarity with statistical software or packages for causal inference (e.g., CausalML, DoWhy, EconML) Familiarity with generative AI tools and large language models (LLMs) for automating insights generation and reporting Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For At Grab We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

Get To Know The Team The Agent Experience PST owns and operates the internal platforms used by Grab's support agents to serve our entire ecosystem- across every vertical and use case. Because agents rely on these tools to resolve critical issues for users and partners, our systems are built for extremely high availability, reliability, and 100% data correctness. When a user needs help with a transaction, they rely on our platform to ensure the agent has the right tools and data at the right time. Get To Know The Role We are looking for a Senior Backend Engineer in Vietnam to contribute to our internal tooling products used for our agents to serve Passengers, Drivers, and Merchants. You will report to the Engineering Manager, who is based in Singapore, and work in an on-site mode. This role is based in Vietnam. The Critical Tasks You Will Perform Design and write with the cutting edge Go language to improve the availability, scalability, latency, and efficiency of Grab's range of services Work with engineering team to explore and create new design / architectures geared towards scale and performance Participate in code and design reviews to maintain our high development standards Engage in service capacity and demand planning, software performance analysis, tuning and optimization Collaborate with product and experience teams to define and prototype feature specifications Work closely with infrastructure team in building and scaling back-end services as well as performing root cause analysis investigations Design, build, analyze and fix large-scale systems Pro-actively debug and solve production incidents during your on-call shift Participate in interview loops to help Grab continue hiring top industry talent Provide technical guidance, mentorship and knowledge sharing to peers. What Essential Skills You Will Need A degree in Computer Science, Software Engineering , Information Technology or related fields 5+ years of experience in software engineering in a distributed systems environment Strong Computer Science fundamentals in algorithms and data structures Familiarity with running large scale web services; understanding of systems internals and networking are a plus Strong understanding of system performance and scaling Proficient in English communication You can be a good coder in any language (C++, C, Java, Scala, Rust, Haskell, OCaml, Erlang, Python, Ruby, PHP, Node.JS, C# etc.), but willing to work on Golang Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

Get to Know the Team The Streaming Data team (a.k.a. Coban) ensures seamless and secure real-time access to continuous events or streams, serving as Grab's unified access pattern for real-time data. We build the infrastructure and platform for writing and consuming real-time data, and provide a cost-effective, managed NoOps service for product and data teams across Grab. We partner closely with sister teams in DataTech to provide integrated data platforms that unlock big data innovation every day. Some examples of the team's work are shared publicly on the Grab Engineering blog: Building a self-serve streaming platform for Kafka topics, Flink, CDC pipelines, Kafka Connect, and notebooks ( An elegant platform ). Making Flink deployments safer through platform guardrails and production deployment patterns ( Safer deployment of streaming applications ). Enabling FlinkSQL exploration and productionisation so users can move faster from streaming ideas to running pipelines ( The complete stream processing journey on FlinkSQL ). Improving Flink release confidence with shadow testing ( Enhancing Flink deployment with shadow testing ). Strengthening real-time Kafka data quality with syntactic and semantic stream contracts ( Real-time data quality monitoring ). These are examples of the platform thinking this role will continue to advance: taking complex real-time infrastructure problems and turning them into reliable, self-service capabilities for Grab teams. Get to Know the Role As a Lead Flink Platform Engineer, you will lead the design, evolution, and operation of Grab's stream processing platform, with Apache Flink as a core compute engine. You will drive medium to large projects across Data Engineering Platforms, mentor senior engineers, and be a technical go-to person for platform architecture, reliability, and production operations. The role is hands-on across Flink, Kafka, AWS cloud infrastructure, Kubernetes, observability, and SRE practices. You will work onsite at Grab Singapore office, One North, and report to the Senior Data Engineering Manager. Why This Role Matters In an agentic world, high-quality real-time signals are becoming even more critical to drive automation, decision-making, and measurable business impact. Apache Flink is a critical real-time infrastructure layer for Grab. By making stream processing easier, safer, and more self-serve, this role helps unlock more real-time signals and turn data into business value across Grab. As Grab embraces agentic engineering, we welcome builders with strong data infrastructure experience and a passion for stream processing to join us. The Critical Tasks You Will Perform Lead platform work that makes stream processing easy, reliable, efficient, and secure across Grab through self-serve capabilities built into the platform. Design and build abstractions, modules, and libraries that lower the barrier to adopting Flink and reduce operational and security toil for users. Improve automation and self-service workflows that allow a small platform team to support many production Flink pipelines at scale. Drive technical design discussions, production readiness reviews, incident learning, and long-term architecture improvements. Partner with Kafka, data lake, metrics, and data governance platform teams to make real-time data pipelines reliable across the broader Grab data ecosystem. Mentor engineers through design reviews, debugging sessions, code reviews, and operational best practices. What Essential Skills You Will Need 5+ years leading teams or projects in software engineering, data engineering, or platform engineering disciplines. Experience building and operating stream processing pipelines in production, preferably with Apache Flink or Spark Streaming. Strong hands-on engineering experience with Kafka and modern programming languages such as Scala or Java. Strong fundamentals in distributed systems, scalable data processing, reliability engineering, and production operations. Ability to lead technical design, mentor engineers, communicate trade-offs clearly, and drive projects from design to production. Excitement to learn, apply new technologies, and improve platform reliability for many internal users. The nice-to-haves: Experience with Kafka Connect, Kubernetes, Go, GitLab CI, AWS, or Terraform. Experience building reusable platform abstractions, SDKs, deployment tooling, or self-service workflows. Experience operating Apache Flink in production, including high availability, checkpointing, safe deployments, and incident response. Experience with a data warehouse or data lake ecosystem such as Spark, Parquet, Iceberg, Delta, or Hudi. Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

Get to Know the Team The AI Platform (AIP) team builds and operates the core ML and AI infrastructure that powers Grab. Our stack spans model serving, ML pipelines, data serving, AI infrastructure, and Applied Research. Together, AI Platform serves hundreds of data scientists and ML engineers across Grab, and is the foundation for everything from fraud detection and search ranking to foundation model efforts, adaptive experimentation, LLM fine-tuning, and the next generation of agent-driven products. Get to Know the Role As a Principal Machine Learning Engineer on AI Platform reporting to the AIP Head of Engineering, you'll be the senior technical anchor and solution architect for the platform. Your mandate is twofold: (1) Raise the ceiling and raise the floor. You'll partner with the Head of Engineering, AI Platform and Applied Research team to shape the forward roadmap, evaluate SOTA techniques worth productionizing, and drive integrations that connect AIP's capabilities into a coherent end-to-end experience. (2) You'll ensure the teams who depend on AIP can actually succeed on it — translating user pain into platform requirements, unblocking complex adoption cases, and getting hands-on where it matters. You'll take on workstreams spanning cross-entity platform consolidation, large-scale training throughput and reliability, and faster, higher-quality model iteration for AIP's most important users. This is an individual contributor role for someone who thrives at the intersection of platform engineering, applied ML, and user empathy — and who is equally comfortable writing a design doc, debugging a training job, and pairing with other teams to land their next model on the platform. The Critical Tasks You Will Perform Solution Architecture for AIP Users: Partner directly with Data Scientists and ML engineers across the company to design end-to-end solutions on AIP. Be the senior technical escalation point for complex adoption cases. Large-Scale Training: Drive the state of large-scale training on AIP — throughput, reliability, cost, and developer experience. Advise and contribute hands-on across foundation model training, RL, simulation, and LLM fine-tuning workloads. Faster Iteration and Model Quality: Attack the end-to-end loop from idea to shipped model — data, training, evaluation, deployment, monitoring. Drive measurable reductions in iteration time and measurable gains in model quality for AIP's highest-value use cases. Platform Integration: Design and drive integrations across AIP surfaces (model serving, ML pipelines, data serving, AI infra, AI Automation tooling) so users experience a coherent platform rather than a collection of services. User Experience Translation: Convert pain points surfaced through embeddings, support channels, and direct user work into functional requirements for AIP teams; propose cross-platform solutions that raise the bar for the DS and MLE personas. Enablement at Scale: Produce reference architectures, patterns, and opinionated best-practice guidance so the next hundred ML use cases land on AIP cleanly, without requiring bespoke platform-team involvement every time. Strategic Roadmap Definition & Mentorship: Stay current with SOTA across ML infrastructure, LLM serving, training systems, and RL; partner with the AIP HoE to decide what Grab should adopt, build, or skip. Raise the technical bar across AIP through code, design reviews, written artifacts, and direct mentorship of senior engineers across the org. What Essential Skills You Will Need Advanced MLOps & ML Platform Engineering: Expert-level mastery of ML lifecycle platforms (e.g., Kubeflow, MLflow, Triton, TorchServe) and distributed training frameworks (PyTorch, Ray, Horovod). Distributed Systems & Infrastructure: At least 10 years of experience in Kubernetes, containerization, and high-performance computing clusters (GPUs/TPUs). Experience optimising large-scale data and model pipelines. Architecture Design: Outstanding system design capability for available, scalable, and secure multi-tenant platform services. AI/LLM System Experience: Hands-on experience with LLM orchestration, fine-tuning infrastructure, or serving optimization (vLLM, TensorRT-LLM). Innovation & AI Fluency: A learning mindset to evaluate and implement state-of-the-art (SOTA) infrastructure paradigms, guiding the team on what to build versus what to skip. Adaptive Execution & Ownership: You can operate independently in high-ambiguity environments, taking full end-to-end accountability for complex system integrations and platform consolidation. Coaching with Care: Commitment to raising the engineering bar. Mentor senior engineers and foster a culture of technical excellence and collaboration. Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

Get to Know the Team The Fulfilment tech family is one of the most important pillars ensuring Grab to out-serve our customers and partners in different businesses and marketplaces across Southeast Asia. We are working on high throughput, real-time distributed systems that use machine learning techniques to solve hundreds of millions of requests per day. Our mission is to offer the best products and experiences to our driver partners as to increase adoption and engagement of our services. Improve driver partner opportunities and efficiency to fulfil customer orders without fail, rain or shine. And to create efficient marketplaces by determining a price that is both sustainable and loved by our partners and customers. Our team members are in Indonesia and Singapore. Get to Know the Role We are seeking experienced and passionate Software Engineers to join our team. You will have opportunities to lead a small team of software engineers to work on multiple backend service clusters. It is very important that our team members to initiatively identify problems and have the right mindset and skills to solve them. You will be reporting to Software Engineering Manager II, Backend and the role will be base onsite in our Petaling Jaya, Selangor, Malaysia office. The Critical Tasks You Will Perform Develop and maintain backend services and APIs with a focus on reliability and scalability. Write efficient database queries (e.g., MySQL, Presto) and optimize system performance through profiling and troubleshooting. Contribute to projects on cloud platforms (AWS, GCP, or Azure) and support deployments via CI/CD pipelines. Implement and maintain automated testing (unit, integration, and end-to-end) to ensure code quality. Gain exposure to distributed computing frameworks such as Apache Flink, while building services in Golang and Scala. Collaborate with senior engineers and cross-functional teams to deliver high-quality technical solutions. What Essential Skills You Will Need More than 4 years of experience in backend development. Familiarity with large-scale distributed web or API services, including systems internals and networking. Hands-on experience with databases and query languages such as MySQL or Presto. Strong understanding of system performance, with ability to profile and resolve bottlenecks. Experience with cloud platforms (AWS, GCP, or Azure), testing frameworks (unit, integration, E2E), and CI/CD pipelines. Hands-on experience using any language and willing to work on Golang and Scala (Flink) Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For At Grab We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

Get to Know the Team The Enterprise Information Security (EIS) team safeguards Grab's corporate infrastructure so our workforce can operate securely and efficiently. We built and enforced defense-in-depth preventative controls spanning Endpoint security, Internet security, Enterprise application security, Data loss prevention, and Corporate Generative AI (GAI) security. Alongside Grabber Technology Solutions (GTS) function, we collaboratively provide a secure and seamless user experience for our Grabbers. Get to Know the Role You are an individual contributor who is responsible to work on complex enterprise security controls and work cross-functionally across Cyber Security, GTS and other departments. Specifically, you will lead an EIS sub-service workstream, and use GAI and automation to shape and scale enterprise security within Grab. If you enjoy solving challenging enterprise security problems, we will give you the opportunity to make a regional impact. You will report to the Senior Manager, Enterprise Information Security and be based onsite at our office based in Petaling Jaya. The Critical Tasks You Will Perform You will manage one of the EIS sub-services workstreams: Endpoint Security, Internet Security, Corporate Data Loss Prevention, and Enterprise Application Security. You will lead and collaborate on cross-functional projects including vulnerability management, shadow IT, data leakage prevention etc. You will enforce security policy and controls to protect our corporate environment, yet enabling business by triaging exceptions via balancing the benefit and trade-offs between security and operations. You will innovate through GAI and automation: Develop and implement automated workflows, MCPs, and custom scripts to automate or "agentify" security tasks, reducing manual overhead and improving response times. What Essential Skills You Will Need Bachelors Degree in Information Security, Cybersecurity or a relevant field 2+ years of relevant working experience Hands-on expertise with industry-standard security tools, specifically Crowdstrike (Falcon, Spotlight), ZScaler (ZIA/ZPA), Google Workspace Native Controls, and Email Security gateways. Proficiency in Unified Endpoint Management (UEM) platforms, including JAMF (macOS) and VMware Workspace ONE (Windows/Mobile). Demonstrable scripting skills (e.g., Python, Bash, or Apps Script) and GenAI (e.g. MCPs) to optimize security operations. Strong problem-solving abilities, excellent communication skills, stakeholder experience, adaptability in fast-paced environments, and a collaborative mindset. Open to fresh graduates Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For At Grab We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

Get to Know the Team The Fulfilment Tech Family builds the systems that power Grab's marketplaces across Southeast Asia. We design real-time, distributed systems and Machine Learning (ML) solutions that process hundreds of millions of requests each day. Our work drives supply allocation, pricing, and order matching for millions of users and driver-partners.Our mission is three-fold: Deliver products that work for our driver-partners Meet consumer demand, regardless of conditions Build marketplaces that balance experience and cost for everyone involved We are looking for a Senior Principal Data Scientist to lead our shift toward automated marketplace optimization. You'll advance how we use data and ML to automate pricing, dispatch, and supply management decisions. Get to Know the Role This is a Senior Principal individual contributor role where you'll build the foundation for autonomous, learning-driven marketplace systems. You'll work at the intersection of reinforcement learning, large language models, and production systems that operate at scale. Your work centres on two areas: Reinforcement Learning (RL) Systems: You'll develop systems that jointly optimize pricing, dispatching, and supply repositioning. You'll build decision agents that handle multiple objectives and adapt when real-world conditions change. LLM-Based Behavioural Intelligence: You'll architect systems using fine-tuned language models to predict, explain, and simulate user decision-making at scale. These models will power the next generation of marketplace automation. You'll serve as the technical lead for a small team, guiding both research direction and production implementation. You'll report to the Head of Data Science and work from Grab's One-North Singapore office. The Critical Tasks You will Perform You'll: Design and implement end-to-end RL systems that combine model-based RL, offline RL, simulation, and online learning into a unified training pipeline. This includes creating state representations and reward structures that balance short-term results with long-term outcomes. Build latent world models and marketplace state representations that capture supply-demand interactions, location-based patterns, and behavioural signals from users and drivers. Develop systems that optimize across multiple marketplace levers simultaneously—pricing, dispatching, and supply repositioning—to expand the set of achievable outcomes for the business. Create policy evaluation frameworks and establish monitoring systems that allow safe deployment of new decision-making policies in production. Fine-tune open-source large language models on domain-specific data to build capabilities for prediction, reasoning, and simulation within marketplace applications. Design and implement training strategies for language models, including supervised fine-tuning, preference-based alignment, and iterative improvement methods. Work with data engineers and backend engineers to integrate RL and LLM systems into real-time production environments serving millions of users. What Essential Skills You Will Need You have PhD in Computer Science, Operations Research, Applied Mathematics, or related field with at least 10 years of experience — to lead complex technical initiatives spanning research and production systems. You are proficient in RL fundamentals — including Markov Decision Processes, stochastic control, and reward design trade-offs. You'll apply these to build closed-loop systems that make sequential decisions in the marketplace. You have experience building production ML/ RL systems with online learning or simulation-based optimization — to deploy models that learn and adapt in real-time environments. You have knowledge in world models and sequential modelling — including latent dynamic models (RNNs, transformers, state-space models) and representation learning for complex systems. You'll use these to model marketplace dynamics accurately. You have hands-on experience fine-tuning large language models in production — including supervised fine-tuning and at least one preference-based alignment method (RLHF, DPO, or GRPO). You'll apply parameter-efficient methods (LoRA, QLoRA, or PEFT) and understand their trade-offs in accuracy, memory, and cost. You can design evaluation frameworks for generative models — including metrics for factual accuracy and reasoning quality. You'll use these to validate model outputs before deployment. You have experience with distributed training frameworks — such as DeepSpeed, FSDP, or Megatron-LM. You'll use these to train large models efficiently across multiple GPUs or nodes. You are proficient in Python and ML frameworks (PyTorch or TensorFlow) — to implement models and integrate with production codebases. You have experience with scalable computing platforms — such as Spark or Ray. You'll use these to process large datasets and distribute training workloads. You can translate ambiguous business problems into concrete modelling tasks — to identify what can be solved with ML/RL and define the scope, data requirements, and success criteria. Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave , and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours What We Stand For At Grab We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.

Get to Know Our Team We are the architects of trust. Our mission is to shield the Grab ecosystem from evolving fraud and safety threats by turning massive datasets into applicable intelligence. From deploying sequence-based models for payment risk to applying graph algorithms that unmask complex money laundering networks, we operate at the intersection of deep learning and platform security. We don't just react; we innovate, researching the latest methods to neutralise tactics before they even surface. Get to Know the Role You will fight fraud by analysing transactional data, developing and deploying machine learning models, and collaborating with cross-functional teams to ensure the seamless integration of fraud detection systems. Reporting to the Data Science Manager II, you will work as an Individual Contributor in a full-time, on-site role at our office in Petaling Jaya, Malaysia. In this role, you will push the boundaries of machine learning and LLM applications at a massive scale, growing your expertise in cutting-edge agentic systems. Apply today to help keep our platform safe and trustworthy! The Critical Tasks You Will Perform You will partner with teams to architect scalable data science solutions that translate complex operational challenges into strategic wins, integrating both traditional ML and agentic LLM systems. You will conduct cutting-edge research to incorporate the latest advancements in algorithms and generative AI into our defense systems to actively counter emerging fraud tactics. You will master data orchestration by preparing, augmenting, and combining diverse data types to build high-fidelity training datasets, including using LLMs to generate synthetic data and labels. You will design, train, and fine-tune model architectures, utilizing a toolkit that includes Graph Neural Networks (GNNs), Transformers, fine-tuned open-source LLMs (like Qwen), and Gradient Boosted Trees. You will lead the end-to-end lifecycle of your models—from production deployment to performance monitoring—iterating alongside software and product engineering teams. You will leverage Generative AI tools (like coding assistants and analytical co-pilots) in your daily workflows to accelerate code generation, automate testing, and enhance your overall productivity. The Essential Skills You Will Need You hold a degree in Computer Science, Physics, Statistics, Mathematics, Engineering, Economics, or a related quantitative field. You have at least 3 years of experience with proficiency in Python and SQL, alongside practical experience using distributed engines like Spark to wrangle and process large-scale datasets. You have hands-on experience building and deploying machine learning models using standard libraries such as TensorFlow, PyTorch, XGBoost, LightGBM, or Scikit-learn. You have practical experience with deep learning architectures (Transformers, RNNs, CNNs) and traditional ML (Boosted Trees), demonstrating the ability to choose the optimal architecture for specific problems. You have an understanding of LLM and Agentic system foundations (e.g., RAG, Transformers) and hands-on experience building or using frameworks like LangGraph, LangChain, or Claude subagents. You have experience engaging with AI tools and emerging technologies to enhance productivity, improve workflows, and contribute new ideas. Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: We have your back with Term Life Insurance and comprehensive Medical Insurance. With GrabFlex, create a benefits package that suits your needs and aspirations. Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. What we stand for at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.