Institute of Foundation Models

Researches and develops foundation models

Machine Learning Engineer - Reinforcement Learning

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeSunnyvale, CA, USAIn-person

About the Job

Requirements

At least 5 years of experience in machine learning systems, infrastructure, or distributed training.
Experience modifying distributed machine learning frameworks such as DeepSpeed, Fully Sharded Data Parallel, FairScale, or Horovod.
Strong software engineering fundamentals, including Python, systems design, and testing.
Multi-node experience with technologies such as Slurm, Kubernetes, or Ray, and debugging skills with NCCL or GLOO.
Ability to implement algorithms across GPUs and nodes based on mathematical specifications.
Experience working on a machine learning platform or infrastructure team, and/or a distributed inference optimization team.
Experience with large-scale machine learning workloads and strong machine learning fundamentals.
Hands-on experience developing a large language model reinforcement learning pipeline, with ownership across rollout/inference and distributed training integration.
Working knowledge of policy optimization methods such as Proximal Policy Optimization or Group Relative Policy Optimization, sufficient to implement and debug sampling, log probabilities, and policy updates.

Responsibilities

Extend or modify distributed training frameworks to support new use cases and architectures.
Connect training workers with rollout-generation engines, including trajectory exchange and policy-weight synchronization.
Create and debug multi-node launch scripts with flexible batch sizes, parallelism strategies, and hardware targets.
Build systems for experiment tracking, job monitoring, and logging that are usable by collaborators and researchers.
Write production-quality code and tests for machine learning infrastructure in PyTorch or JAX, ensuring reliability and maintainability at scale.
Implement reward/verifier integration and trajectory processing, and validate log probabilities, token masks, and loss inputs with researchers.
Coordinate rollout and training workers, including checkpoint/restart and failure recovery; track policy versions and sample staleness in asynchronous execution.

Desired Qualifications

Exposure to mixed-precision training, such as bfloat16 or float8, with accuracy validation.
Familiarity with performance profiling, kernel fusion, or memory optimization.
Open-source contributions or published research in venues such as MLSys, ICML, or 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 reinforcement learning, multi-turn or tool-using rollout environments, or custom reward/verifier systems.

Salary Range: 150000 to 450000 (Currency: USD) (Pay period: per-year-salary)

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.

Compensation

$150,000 – $450,000/year + discretionary performance bonus + benefits

The posted salary range represents the company’s good faith estimate of the base compensation for this position upon hire. The actual base compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, and specific business needs. In addition to base salary, total compensation includes an annual discretionary performance bonus and comprehensive benefits.

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

About the Company

Institute of Foundation Models

Researches and develops foundation models

IFM builds and studies foundation models—large AI models designed to learn broadly from diverse data and be used across many tasks. Its work centers on academic research to create open, fast, and practical models that address real-world societal needs, rather than narrow applications. The models are developed by a global team across Abu Dhabi, Paris, and Silicon Valley, with a strong emphasis on openness and collaboration to advance AI science and accessibility. Unlike typical private labs that lock models behind paywalls, IFM aims to provide publicly accessible, efficient models that can be used by researchers and developers to solve real problems. The overarching goal is to push the science of foundation models forward while ensuring their benefits reach society at large.

Company size: Not listed
Company stage: Not listed
Total funding: Not listed
Headquarters: United Arab Emirates
Founded: Not listed

Recently Posted Jobs

Explore more opportunities at Institute of Foundation Models

See more jobs