Machine Learning Engineer — Reinforcement Learning

About The Role

About the Institute of Foundation Models We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy.

As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers.

Key responsibilities

• Distributed Framework Ownership – Extend or modify training frameworks (e.g., DeepSpeed, FSDP) to support new use cases and architectures.

• Training & Inference Integration – Connect training workers with rollout generation engines (e.g., vLLM, SGLang), including trajectory exchange and policy-weight synchronization.

• Launch Config & Debugging – Create and debug multi-node launch scripts with flexible batch sizes, parallelism strategies, and hardware targets.

• Metrics & Monitoring – Build systems for experiment tracking, job monitoring, and logging usable by collaborators and researchers.

• Infra Engineering – Write production-quality code and tests for ML infra in PyTorch or JAX; ensure reliability and maintainability at scale.

• RL Pipeline Development – Implement reward/verifier integration and trajectory processing, and validate log probabilities, token masks, and loss inputs with researchers.

• RL Execution & Recovery – Coordinate rollout and training workers, including checkpoint/restart and failure recovery; track policy versions and sample staleness when using asynchronous execution.

Qualifications And Requirements

Must-Haves:

• 5+ years of experience in ML systems, infra, or distributed training

• Experience modifying distributed ML frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)

• Strong software engineering fundamentals (Python, systems design, testing)

• Proven multi-node experience (e.g., Slurm, Kubernetes, Ray) and debugging skills (e.g., NCCL/GLOO)

• Ability to implement algorithms across GPUs/nodes based on mathematical specs

• Experience working on an ML platform/ infrastructure, and/or distributed inference optimization team

• Experience with large-scale machine learning workloads (strong ML fundamentals)

• Hands-on experience developing an LLM RL pipeline, with ownership across rollout/inference and distributed training integration.

• Working knowledge of policy optimization methods such as PPO or GRPO, sufficient to implement and debug sampling, log probabilities, and policy updates.

Nice-to-Haves:

• Exposure to mixed-precision training (e.g., bf16, fp8) with accuracy validation

• Familiarity with performance profiling, kernel fusion, or memory optimization

• Open-source contributions or published research (MLSys, ICML, NeurIPS)

• CUDA or Triton kernel experience

• Experience with large-scale pre-training

• Experience building custom training pipelines at scale and modifying them for custom needs

• Deep familiarity with training infrastructure and performance tuning

• Experience with asynchronous RL, multi-turn or tool-using rollout environments, or custom reward/verifier systems.

Additional information

Visa Sponsorship This position is eligible for visa sponsorship.

Benefits Include *Comprehensive medical, dental, and vision benefits *Bonus *401K Plan *Generous paid time off, sick leave and holidays *Paid Parental Leave *Employee Assistance Program *Life insurance and disability

Must-Haves:

• 5+ years of experience in ML systems, infra, or distributed training

• Experience modifying distributed ML frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)

• Strong software engineering fundamentals (Python, systems design, testing)

• Proven multi-node experience (e.g., Slurm, Kubernetes, Ray) and debugging skills (e.g., NCCL/GLOO)

• Ability to implement algorithms across GPUs/nodes based on mathematical specs

• Experience working on an ML platform/ infrastructure, and/or distributed inference optimization team

• Experience with large-scale machine learning workloads (strong ML fundamentals)

• Hands-on experience developing an LLM RL pipeline, with ownership across rollout/inference and distributed training integration.

• Working knowledge of policy optimization methods such as PPO or GRPO, sufficient to implement and debug sampling, log probabilities, and policy updates.

Nice-to-Haves:

• Exposure to mixed-precision training (e.g., bf16, fp8) with accuracy validation

• Familiarity with performance profiling, kernel fusion, or memory optimization

• Open-source contributions or published research (MLSys, ICML, NeurIPS)

• CUDA or Triton kernel experience

• Experience with large-scale pre-training

• Experience building custom training pipelines at scale and modifying them for custom needs

• Deep familiarity with training infrastructure and performance tuning

• Experience with asynchronous RL, multi-turn or tool-using rollout environments, or custom reward/verifier systems.

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