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Medra is building Physical AI Scientists—robotic systems that enable scientific breakthroughs by combining physical automation with advanced AI reasoning. The company has shipped production systems, raised a $52M Series A, and is opening one of the largest autonomous labs in the US.
In this role, you will define and execute post-training strategies for AI models used in live scientific workflows. You'll own the full post-training stack: designing problem formulations and success metrics, engineering large-scale data collection pipelines (internal and public), running ML experiments, and integrating trained models into production. You'll create rigorous evaluations to measure whether models genuinely improve scientific protocols and assay development. You'll develop agentic systems with context management and custom tool calls to surface new insights about experimental design in real lab environments. Close collaboration with scientists, robotics engineers, and operations teams is central—your work directly impacts how leading biopharma partners conduct R&D. You'll also help shape the engineering culture and technical direction of a new machine learning team.
The team is small, ambitious, and composed of engineers from Tesla, Amazon, SpaceX, and Neuralink. The culture emphasizes collaborative problem-solving, moving fast, and resolving disagreements quickly and empathetically.
REQUIREMENTS:
- Practical experience building AI-driven workflows into real-world systems
- Strong problem-solving skills for debugging complex systems
- Clear grasp 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 when loosely defined
- Proficiency in Python and familiarity with at least one deep learning framework (PyTorch, JAX, or equivalent)
- Experience with LLMs, post-training, reinforcement learning, or agentic systems
