Machine Learning Engineer

About this role

Build the engineering backbone for a self-improving machine learning system focused on healthcare, including training pipelines, reward design, and safeguards against model degradation. This role combines production ML infrastructure with deep reinforcement learning expertise to ensure feedback loops remain fast, reliable, and trustworthy.

What you'll do

• Design and implement training, evaluation, and deployment loops with emphasis on reproducibility and reliability
• Engineer reward and feedback signals while detecting and mitigating reward hacking and specification gaming
• Develop evaluation harnesses and metrics before model development to ensure measurement integrity
• Own end-to-end data pipelines and automated flywheels that power the learning loop
• Debug model regressions and stabilize non-stationary training and feedback systems
• Partner with research and product teams to translate methods into production-ready systems

What they're looking for

• Python and production-quality ML training code
• Data pipelines and distributed/large-scale training
• Reinforcement learning fundamentals (reward modeling, credit assignment, on/off-policy tradeoffs)
• RLHF, RLAIF, or active-learning system implementation
• Model debugging and performance regression analysis
• Experiment tracking and evaluation framework design
• Non-stationary systems and feedback loop failure mode analysis
• LLM fine-tuning or agentic evaluation systems