Machine Learning Engineer

Hippocratic AI

. Build and maintain training, evaluation, and deployment loops for the recursive self-improvement system .

Posted 10/9/2026full-timeUnited StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining machine learning systems, with a strong focus on data pipelines, feedback loops, and model evaluation. Proficient in Python and experienced in deploying robust ML solutions in production environments.

Highest-signal resume keywords

Python ProgrammingMachine Learning System DeploymentData Pipeline ManagementFeedback Loop DesignReward Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData PipelinesModel EvaluationDebuggingLLM Fine-TuningActive LearningReinforcement LearningExperiment TrackingContinual LearningReward Modeling

Certifications & Qualifications

BS in Computer SciencePhD in Reinforcement LearningMS in Machine Learning

Industry Keywords

Reinforcement LearningLarge-Scale ML InfrastructureFeedback SignalsSpecification GamingNon-Stationary Systems

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Build and maintain training, evaluation, and deployment loops for the recursive self-improvement system
Design and implement reward and feedback signals
Investigate and mitigate reward hacking, specification gaming, and distribution drift
Build evaluation harnesses and metrics
Own data pipelines and automated data flywheels
Debug model-quality regressions
Stabilize non-stationary training and feedback loops
Collaborate with research and product to turn methods into robust, shippable systems

Requirements

What you’ll need

Excellent Python and clean, well-tested ML training code
Solid grasp of data pipelines, distributed / large-scale training, and experiment tracking
Ability to debug silent model-quality regressions
Hands-on experience with a feedback or learning loop, including a reward model, evaluation harness, RLHF/RLAIF or active-learning data pipeline, retraining or continual-learning pipeline, LLM fine-tuning with human or AI feedback, or agentic evaluation harnesses
Working knowledge of reward modeling, on-policy vs. off-policy tradeoffs, and credit assignment
Ability to reason about reward hacking, specification gaming, feedback loops amplifying errors, and non-stationary systems
Experience shipping an ML system into production and maintaining it over time
Minimum BS in Computer Science
Hands-on experience in ML
PhD or MS in RL / ML paired with real production experience is nice to have
Experience at a lab or company doing RLHF, agents, or large-scale ML infrastructure is nice to have
Familiarity with LLM fine-tuning, evaluation frameworks, or agent orchestration is nice to have
Legal authorization to work in the country of the position
Must answer whether able to work onsite in Menlo Park, CA, 5 days per week for 8 hours per day
Visa sponsorship may be required

Benefits

Comp & perks

Equal opportunity employment
Hiring-process accommodations available
Global healthcare and AI mission
Opportunity to work with leading healthcare and AI experts
Opportunity to work on a healthcare-only, safety-focused LLM
Opportunity to contribute to a company backed by leading healthcare and AI investors