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# Machine Learning Engineer -- Reinforcement Learning

- **Company:** Institute Inc
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Computer Clusters, Software Code Optimization, Profiling, Nvidia CUDA, Software Debugging, Distributed Computing Environment, Python (Programming Language), Machine Learning, Node.Js, Performance Tuning, Tensorflow, Azure Machine Learning, Reinforcement Learning, Data Logging, Graphics Processing Unit (GPU), Pytorch, DeepSpeed, Large Language Models, Reliability of Systems, Kubernetes, Production Code, SGLang, Free and Open-Source Software, Build Tools, Slurm, Machine Learning Operations, Data Pipelines
- **Published:** October 11, 2026
- **Apply:** https://www.juju.com/job/16_83d7face

## About the Role

* 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.

## Description

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.