29d ago

USD 150,000–450,000 per year

Machine Learning Engineer — Reinforcement Learning

United States·SunnyvaleFull-timemid

Machine Learning EngineerData

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Quick Summary

Overview

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,

Technical Tools

Machine Learning EngineerData

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.

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.

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.

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.

What We Offer

~1 min read

$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.

✓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

Location & Eligibility

Where is the job

Sunnyvale, United States

On-site at the office

Who can apply

About this job

Pay per year: $150,000–$450,000 a year; 43% above the $209,500 median for Machine Learning Engineer jobs on Jobera (968 salaries).
Who can apply: United States
Posted: 29 days ago
Employer: has 43 open jobs on Jobera in 3 countries.