Research Engineer, Post-training

Job Description

### **What We're Building:**

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.

### **The Team:**

* 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.

### In this role, you will:

* 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