About the job
As the Senior Product Manager for Agentic Science, you will sit at the intersection of drug discovery and AI, serving as the critical translator between our scientific teams and the agentic systems they depend on. Your primary focus will be on outcomes: ensuring our agents impact Recursion’s pipeline through demonstrably accelerating discovery. This role is not about maintaining a static roadmap; it is about navigating the frontier of a rapidly evolving field. You will partner closely with drug discovery scientists to understand their workflows, translate their needs into agent task definitions, and design and maintain the benchmarks that -ensure our agents are driving value. As the field evolves rapidly, you will help the team distinguish real scientific progress from superficially promising results.
Responsibilities
Champion Benchmarking for Agentic Science: Drive alignment across scientific and technical teams around evaluation frameworks that measure agent performance against scientifically meaningful outcomes, continuously refining them as the field evolves.
Drive Outcome-Focused Product Development: Keep the team anchored to what matters: building agents that meaningfully advance drug discovery programs, not just executing tasks.
Evangelize the "Human-in-the-loop" Evolution: Work with scientific stakeholders to define interfaces where humans review, validate, and shape agent reasoning, ensuring our scientists evolve from "operators" to "architects" of discovery.
Monitor the Competitive Benchmark Landscape: Track how leading organizations across pharma AI, biotech, and foundation model research are measuring agentic performance. Ensure Recursion's evaluation frameworks stay calibrated against external standards, so our benchmarks reflect genuine scientific progress rather than internally optimized metrics.
Qualifications
Minimum
Background in Drug Discovery or AI-driven Science: You have direct experience working in drug discovery, biotech, or AI-driven scientific research. Ideally, you will have worked hands-on with agentic systems (or agents) in a scientific context. You can credibly partner with PhD-level scientists and translate between scientific goals and technical systems without losing fidelity on either side.
Fluency in Modern AI: You don't need to be an engineer, but you follow the AI landscape closely and understand how LLMs and agentic systems actually work in practice. Bonus points if you've benchmarked an agent(s) or worked hands-on with agentic tools, even outside a formal work context.
Experimentation-First Mindset: You have a natural instinct for working through rapid prototyping cycles, whether in a product, research, or scientific context. You'd rather run a quick experiment than write a long spec.
Systems Thinking: You can hold the big picture in mind while working in the details. You think naturally about how multiple agents interact, where they hand off to each other, and where humans need to be in the loop to catch errors, validate outputs, or make judgment calls that agents can't.
Communication & Influence: Strong written and oral skills to align diverse stakeholders (PhDs in Biology, ML and software engineers, AI Researchers) around a unified technical vision.
Preferred
Background in Drug Discovery or AI-driven Science: You have direct experience working in drug discovery, biotech, or AI-driven scientific research. Ideally, you will have worked hands-on with agentic systems (or agents) in a scientific context. You can credibly partner with PhD-level scientists and translate between scientific goals and technical systems without losing fidelity on either side.
Fluency in Modern AI: You don't need to be an engineer, but you follow the AI landscape closely and understand how LLMs and agentic systems actually work in practice. Bonus points if you've benchmarked an agent(s) or worked hands-on with agentic tools, even outside a formal work context.
Experimentation-First Mindset: You have a natural instinct for working through rapid prototyping cycles, whether in a product, research, or scientific context. You'd rather run a quick experiment than write a long spec.
Systems Thinking: You can hold the big picture in mind while working in the details. You think naturally about how multiple agents interact, where they hand off to each other, and where humans need to be in the loop to catch errors, validate outputs, or make judgment calls that agents can't.
Communication & Influence: Strong written and oral skills to align diverse stakeholders (PhDs in Biology, ML and software engineers, AI Researchers) around a unified technical vision.