Machine Learning Engineer
Hippocratic AI7 hours ago
Responsibilities
- Build and maintain reproducible, reliable training, evaluation, and deployment loops.
- Design reward and feedback signals and mitigate reward hacking, specification gaming, and distribution drift.
- Build evaluation harnesses and metrics before model development.
- Own data pipelines and automated data flywheels for the learning loop.
- Debug model-quality regressions and stabilize non-stationary training and feedback loops.
- Collaborate with research and product teams to turn methods into production systems.
Requirements
- Strong machine learning engineering fundamentals and excellent Python skills.
- Experience writing clean, well-tested ML training code and shipping production ML systems.
- Knowledge of data pipelines, distributed or large-scale training, and experiment tracking.
- Hands-on experience with a feedback or learning loop, such as a reward model, evaluation harness, RLHF/RLAIF system, active-learning system, or continual-learning pipeline.
- Working knowledge of reward modeling, on-policy and off-policy tradeoffs, credit assignment, and feedback-system failure modes.
- Ability to evaluate non-stationary systems and debug why model quality degrades.
- Preferred experience with RLHF, agents, large-scale ML infrastructure, LLM fine-tuning, evaluation frameworks, or agent orchestration.
- An MS or PhD in reinforcement learning or machine learning paired with production experience is preferred but not required.
Benefits
- Opportunity to work on a safety-focused healthcare AI platform.
- Collaboration with experts from healthcare, research, and major technology institutions.
- Equal opportunity employer committed to an inclusive workplace and accommodations during hiring.
Tech Stack
Categories
About Hippocratic AI
Hippocratic AI builds a safety-focused large language model and AI agents for healthcare workflows, used by health systems for patient outreach, post-discharge follow-up, and chronic-care management. It licenses its platform and tools to providers to automate and scale clinical support tasks while meeting health-system requirements. Founded in 2023 and headquartered in Palo Alto, the company is privately held.