Solid understanding of probability, statistics, and machine learning fundamentals
Ability to manage the entire post-training stack
Proficiency in Python and familiarity with deep learning frameworks like PyTorch or JAX
Experience with large language models and reinforcement learning
Responsibilities
Define post-training recipes for AI models, including problem selection and measurement
Build and manage post-training data pipelines using internal and public data
Create evaluations to assess improvements in scientific protocols
Develop systems for context management and tool calls to enhance experimental design
Collaborate with scientists and engineers to integrate reasoning capabilities into experiments
Shape the engineering culture and direction of a new machine learning team
Benefits
Opportunity to work at the intersection of AI and life sciences
Collaborative environment with scientists and engineers
Chance to influence the technical direction of a new team
Engagement with leading biopharma partners
Focus on innovative solutions in R&D
Full Job Description
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
