ML Systems Research Engineer, RL / Inference / Agent Systems
Core
Build reinforcement learning, inference, and evaluation infrastructure for AI-for-engineering systems to enable agents and models to improve real engineering workflows.
Role type
Senior ML Systems Research Engineer (RL/Inference/Agent Systems)
Builds
Scalable ML systems, job orchestration, evaluation platforms, and distributed experimentation infrastructure for production engineering teams.
Domain
AI/ML Systems, Reinforcement Learning, Hardware Engineering Automation
Deliverable
production ML models
Required skills
Python, PyTorch/JAX/TensorFlow, Reinforcement Learning, Model Inference, Job Orchestration, Distributed Systems, GPU Computing (CUDA/ROCm), Experiment Tracking, Reward Modeling
Preferred skills
RLHF/GRPO/Preference Optimization, LLM Agents/Tool-use, Kubernetes/Ray/Slurm, Hardware Engineering Workflows, Publications in ML Systems
Technologies
PyTorch, JAX, TensorFlow, Kubernetes, Ray, Slurm, CUDA, ROCm/HIP
Responsibilities
Build RL and inference systems for agentic engineering workflows; Develop infrastructure for long-horizon and high-latency reward tasks; Design staged rewards and proxy graders; Support optimization workflows with candidate generation and benchmark execution; Partner with AI research scientists on reward hacking and post-training methods; Build scalable inference and tool-use pipelines for LLM agents; Standardize datasets and evaluation definitions.
Seniority
Senior, hands-on IC
