Recursionpharmaceuticals

Your work will change lives. Including your own. The Impact You’ll Make As a computational biology specialist on our Target ID team, you will be at the forefront of transitioning Recursion to its next era of drug discovery. You will serve as a critical biological anchor for a highly technical data science team building the next generation of our target discovery pipelines. The team’s mission is to identify novel therapeutic targets at scale across the genome for hundreds of indications. You will have access to all of Recursion's data layers—including massive internal maps, functional genomics, and rich patient data (transcriptomics, genetics, and Real-World Data/EHR)—and will be tasked with proposing, piloting, and deploying new methods to integrate these datasets. Crucially, you will leverage your biological subject matter expertise to orient and focus your data scientist and software engineer peers towards reliable and correct usage of biological data and feasible candidate programs. As we build out semi-automated and agentic tools (e.g., multi-agent LLM systems for portfolio oversight and target assessment), your deep real-world biology experience will guide the development of these tools, ensuring they are grounded in biological reality and translate to meaningful portfolio and patient impact. The ideal candidate is a computational biology specialist with high fluency in data science tech stacks. You know how to build and evaluate predictive models and build agentic pipelines, and your differentiator is your deep understanding of patient-relevant datasets, disease biology, and target discovery. In this role, you will: - Discover & Evaluate: Propose and validate novel targets using deep integration of patient data (transcriptomics, population genetics, EHR, etc) and Recursion's internal multi-omic data layers. - Guide & Automate: Collaborate closely with data scientists to build, refine, and guide semi-automated and agentic target discovery tools. You will ensure these tools are biologically sound and can scale to evaluate hypotheses across many indications spanning a wide range of therapeutic areas. - Triangulate: Use advanced statistical methods (e.g., causal inference, survival modeling) to establish confident target-to-patient connections and define specific addressable patient populations for early pipeline programs. - Bridge the Gap: Translate complex platform findings into disease-relevant applications, bridging the gap between high-dimensional data science output and actionable drug discovery insights. - Present & Influence: Communicate complex biological rationale and data analyses to decision-makers and cross-functional stakeholders, driving data-backed "go/no-go" decisions in a two-stage target approval process. The Team You’ll Join Our group is a bold, agile, diverse collective of data scientists and computational biologists driving Recursion’s early portfolio strategy. We are focused on aggressively expanding our early-stage pipeline with highly validated, novel therapeutic targets. To achieve this, we prioritize defining specific patient populations and establishing clear, data-backed translational paths from day one of every new program. Because this

Your work will change lives. Including your own. The Impact You’ll Make Recursion is leading an era of autonomous science – iterating across the discovery process, leveraging machine learning and agents built-for-purpose to uncover novel insights across biology and chemistry, fueling our clinical-stage pipeline. 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. In this role, you will: - 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. The Team You’ll Join You will join a cross-functional team of software engineers, data scientists, AI/ML scientists and drug discovery biologists and chemists who build the technical bedrock that enables autonomous science, including agent orchestration, guardrails, and the connectivity between our digital and physical assets. You will work closely with the Discovery teams (the users of these agents), the AI Research teams (who build cutting-edge models), and our automated biology and chemistry lab teams (who generate the data that feeds into the models). The Experience You’ll Need - 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