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

Let's talk if you have:

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