Machine Learning Engineer — Reinforcement Learning
Institute of Foundation Models Open · verified Oct 22, 2026 On-site Sunnyvale Full-time Mid-level Machine Learning Engineering
At a glance
Mid-level Machine Learning Engineering role at Institute of Foundation Models. Sunnyvale · full-time · $150,000–$450,000 base.
SalaryStated by Institute of Foundation Models
$150,000 – $450,000
Base salary for this role, in USD per year, as published in the posting.
This role, from Institute of Foundation ModelsOur estimate for the market: $217,400–$302,625Median $259,100
Worked out from 8 postings that stated pay, median $259,100. Last updated Sep 29, 2026.
RoleMachine Learning Engineering
SeniorityMid-level
LocationSunnyvale
WorkplaceOn-site
EmploymentFull-time
Base pay$150,000–$450,000
PostedOct 5, 2026 · 2w ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Extend distributed training frameworks to support new model architectures and use cases
- Connect training workers with rollout engines, passing trajectories and keeping policy weights in sync
- Build and debug multi-node launch setups with flexible batch sizes, parallelism and hardware targets
- Create experiment tracking, job monitoring and logging systems for researchers and collaborators
- Implement reward and trajectory handling, then coordinate checkpoint recovery and policy-version tracking
What you bring
- At least five years in ML systems, infrastructure or distributed training
- You have modified distributed ML frameworks such as DeepSpeed, FSDP or another listed framework
- You bring multi-node experience, strong Python and systems design skills, and debugging ability
- You have built an LLM reinforcement-learning pipeline spanning rollout or inference and distributed training
- You can implement and debug sampling, log probabilities and policy updates using methods such as PPO or GRPO
Who this fits
This mid-level, hands-on infrastructure role suits an engineer with experience across LLM reinforcement-learning pipelines and distributed training. The position is onsite in Sunnyvale.
From the employer
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
- 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.
- 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.