AI / Reinforcement Learning Engineer

About the Opportunity
A venture-backed medical device company is developing a novel therapy platform for treating complex cardiac disorders.

The company has demonstrated promising early results and is now advancing toward a clinically deployable system. This is a rare opportunity to join a highly technical, mission-driven team working at the intersection of machine learning, control systems, and human physiology.

Role Overview

This role will lead the development of an adaptive, reinforcement learning–based control system within a dynamic and partially observable biological environment.

You will work on a complex sequential decision-making problem, where the system must interpret high-dimensional physiological signals, predict outcomes of interventions, and continuously optimize performance in real time.

The role includes both simulation-based development and collaboration with experimental teams to translate algorithms into real-world applications.

Key Responsibilities

• Design and implement reinforcement learning algorithms for adaptive control
• Develop state representations and reward functions for complex physiological systems
• Build and evaluate model-based, offline, or constrained RL approaches
• Utilize simulation environments to train, test, and validate algorithms
• Develop signal processing pipelines for time-series physiological data
• Apply control theory principles to ensure system stability and safety
• Partner cross-functionally with engineering and research teams to support system development and validation

Required Qualifications

• MS or PhD in Electrical Engineering, Computer Science, Biomedical Engineering, or related field
• Strong expertise in reinforcement learning and/or control systems
• Experience with Python (e.g., PyTorch, TensorFlow)
• Background in signal processing and time-series analysis
• Experience working with complex, dynamic systems

Preferred Qualifications

• Experience applying ML/RL to physical or safety-critical systems (e.g., robotics, autonomous systems)
• Exposure to healthcare, medical devices, or physiological data
• Familiarity with optimization methods (e.g., Bayesian optimization, evolutionary algorithms)
• Understanding of system validation, robustness, and safety considerations

Pay: From $100,000.00 per year

Benefits:

• Health insurance

Work Location: In person