Where is the Machine Learning Engineer — Reinforcement Learning role at Ifm Us located?

The Machine Learning Engineer — Reinforcement Learning position at Ifm Us is based in Sunnyvale, CA. This is an on-site position.

What type of employment is the Machine Learning Engineer — Reinforcement Learning role at Ifm Us?

The Machine Learning Engineer — Reinforcement Learning position at Ifm Us is a Full time role.

What experience is required for the Machine Learning Engineer — Reinforcement Learning role at Ifm Us?

The Machine Learning Engineer — Reinforcement Learning position at Ifm Us requires 5+ yrs exp.

What skills are required for the Machine Learning Engineer — Reinforcement Learning role at Ifm Us?

Key skills for the Machine Learning Engineer — Reinforcement Learning position at Ifm Us include: machine learning, deep learning, node.js, pytorch, python, kubernetes, large language models.

Machine Learning Engineer — Reinforcement Learning — Ifm Us

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.

The Role

We’re looking for an RL infrastructure engineer to help extend and scale our end-to-end RL training systems. You’ll work side-by-side with world-class researchers and engineers to:

Extend distributed training frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod)
Integrate rollout generation, reward computation, trajectory processing, policy updates, and weight synchronization
Build robust config + launch systems across multi-node, multi-GPU clusters
Own experiment tracking, metrics logging, and job monitoring for external visibility
Improve training system reliability, maintainability, and performance

Hands-on experience developing end-to-end RL infrastructure is required. Strong infrastructure and systems experience is what we value most.

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

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.

Machine Learning Engineer — Reinforcement Learning

Job description