Senior ML Production Model Automation Engineer

What you'll do

• Build and manage end-to-end model pipelines for rapid iteration, staging promotion, and production rollout.
• Design and operate agent-based automation pipelines where agents handle decision logic at various gates.
• Develop multi-agent workflows using LLM-native tooling for on-device evaluation and automated root cause analysis.
• Create shell scripts and CLI commands to convert configurations and datasets into active training jobs.
• Manage the operational infrastructure for large model training including SFT, LoRA adapter, and RL phases.
• Build tools that improve developer ergonomics and simplify onboarding for internal engineering teams.
• Ensure system observability and reliability across distributed training environments and cloud platforms.

What we're looking for

• Strong software engineering fundamentals with proficiency in Python and Bash.
• 5+ years of experience in Machine Learning Operations (MLOps).
• Production experience with cloud ML platforms including GCP TPU, AWS GPU clusters, or Kubernetes-backed training infrastructure.
• Familiarity with the ML training lifecycle, including data preprocessing, distributed training, and checkpoint formats.
• Experience with infrastructure-as-code, CLI tool design, and developer ergonomics.
• Hands-on experience with JAX, XLA, or large-model training stacks.
• Background in MLOps tools such as model registries, feature stores, and experiment trackers.
• A Bachelor's degree in Computer Science or an equivalent technical discipline.