Research Engineer, Post-training

If you want to shape the future of science, come build with us. 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.

🤖 Physical AI that can operate scientific instruments with human-level dexterity.

🧪 Scientific AI that can analyze results, reason about next steps, and close the loop autonomously.

We shipped our first production system over a year ago, recently raised a $52M Series A, and are opening one of the largest autonomous labs in the US. We're a small, ambitious team and you'd be joining early.

• We’re a team of passionate, mission-driven engineers from companies like Tesla, Amazon, SpaceX, and Neuralink. We’re collaborative and love moving fast, both with our product and on team trips skiing or go-karting!

• As a team, we love nerding out about engineering and robotics — plus other topics like race cars or cooking. We like learning new things and then sharing our new knowledge with each other.

• Our team is opinionated and straightforward. We don’t mind intense discussions about design tradeoffs. If we have arguments or miscommunication, we resolve conflicts quickly and empathetically.

• Define post-training recipes for our AI models — from deciding which problem matters and how to measure it, to engineering large data collections, to running ML experiments, to integrating post-trained models into production workflows

• Build and own the post-training data pipelines integrating both internal data and public data

• Create meaningful and trustworthy evaluations that tell us whether our models are improving scientific protocols and assay development

• Develop agentic systems with context management and custom tool calls to surface new scientific insights about experimental design in real lab environments

• Work closely with scientists, robotics engineers, and operation teams to bring reasoning capabilities into live experimental loops for leading biopharma partners

• Shape the engineering culture and technical direction of a new machine learning team that's redefining how life science R&D gets done

• Practical experience building AI-driven workflows into the real world

• Strong problem solving skills for debugging complex systems

• A 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 things are loosely defined

• Proficiency in Python and familiarity with at least one deep learning framework (e.g., PyTorch, JAX)

• Experience with LLMs, post-training, reinforcement learning, or agentic systems