Research Engineer, Post-training

Medra • San Francisco, CA

1d

About The Position

Medra is building Physical AI Scientists: robotic systems that work hand-in-hand with leading biopharma partners to enable scientific breakthroughs faster than ever before. They are developing Physical AI that can operate scientific instruments with human-level dexterity and Scientific AI that can analyze results, reason about next steps, and close the loop autonomously. The company shipped its first production system over a year ago, recently raised a $52M Series A, and is opening one of the largest autonomous labs in the US. The team is described as small, ambitious, and composed of passionate, mission-driven engineers from companies like Tesla, Amazon, SpaceX, and Neuralink. They value collaboration, moving fast, and continuous learning. The team engages in intense discussions about engineering and robotics, resolving conflicts quickly and empathetically.

Requirements

• Practical experience building AI-driven workflows into the real world.
• Strong problem-solving skills for debugging complex systems.
• Clear understanding of probability, statistics, and ML fundamentals.
• Ability to own the post-training stack end-to-end: data pipelines, harnesses, RL environments, and agentic evaluations, even with loosely defined requirements.
• Proficiency in Python.
• Familiarity with at least one deep learning framework (e.g., PyTorch, JAX).

Nice To Haves

• Experience with LLMs.
• Experience with post-training.
• Experience with reinforcement learning.
• Experience with agentic systems.

Responsibilities

• Define post-training recipes for AI models, including problem identification, measurement strategies, data collection engineering, ML experiment execution, and integration of post-trained models into production workflows.
• Build and manage post-training data pipelines that integrate both internal and public data.
• Create robust evaluations to assess model improvement in scientific protocols and assay development.
• Develop agentic systems with context management and custom tool calls to uncover new scientific insights for experimental design in real lab environments.
• Collaborate with scientists, robotics engineers, and operations teams to integrate reasoning capabilities into live experimental loops for biopharma partners.
• Contribute to shaping the engineering culture and technical direction of a new machine learning team focused on revolutionizing life science R&D.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: No Education Listed