Design, build, and maintain infrastructure for LLM post-training, evaluation, and deployment.
Own scalable pipelines for training orchestration, rollout generation, reward and feedback processing, checkpointing, and experiment management.
Build reliable systems for feedback-driven model improvement, including human or AI feedback loops, large-scale offline evaluation, and regression detection.
Partner closely with research scientists to turn new post-training methods into reusable engineering workflows.
Collaborate with agent engineers and platform teams to integrate training and evaluation systems with production model and agent stacks.
Optimize distributed training and inference workloads for reliability, throughput, cost efficiency, and observability.
Drive best practices for reproducibility, versioning, monitoring, deployment, and operational excellence across ML systems.

5+ years of experience in software engineering, ML systems, or distributed infrastructure.
Strong proficiency in Python and experience building production systems or large-scale ML pipelines.
Hands-on experience building infrastructure for model training, post-training, evaluation, or serving.
Experience designing reliable, scalable systems for distributed and GPU-based workloads.
Strong debugging skills across systems, pipelines, and model-facing failures.
Experience building infrastructure for LLM post-training, including RLHF, preference optimization, reward modeling, or related feedback-driven training workflows.
Experience working cross-functionally with research scientists and engineers.
Familiarity with cloud platforms (AWS, GCP) and containerized environments (Docker, Kubernetes).

Lead Machine Learning Engineer, LLM Infrastructure

Key skills