The RoleThis is a builder-integrator role inside a deeply technical, cross-domain environment spanning AI/ML, engineering, computational chemistry, biology, and wet lab operations.
You will serve as the connective tissue and acceleration engine across these domains.
You will work closely with senior leadership to own roadmap definition and execution across critical internal platforms while driving structural improvements in how our teams operate.
This role exists to:- Bridge domain silos and reduce friction between ML experimentation and biological context
- Operate at both the strategic systems level and the fiddly operational detail level
- Get hands dirty when needed
- Close the AI 14 Chem loop so lab data is captured in standardized form, accessible for model training, and drives measurable model performance improvements
- Increase iteration speed by an order of magnitude from research idea to validated result
- Standardize execution across programs so we can scale programs without reinventing workflows
The products you19ll work on include:- Nucleus, our internal platform that houses the GEMS AI system that enables all of our drug discovery programs.
- Our computational methods research platform - the data, analysis and pipelining platform that powers new physics and AI methods development.
- You will own the product roadmap, prioritize initiatives, and drive the execution of projects that support our mission to revolutionize drug discovery.
Responsibilities- Roadmap Ownership: Define, maintain, and communicate the technical and scientific product roadmap in alignment with company goals, ensuring prioritization of impactful projects.
- User research and design: Deeply understand user needs, translate workflow complexity into simple and effective product design.
- Team Collaboration and Translating Across Domains: Facilitate effective collaboration among software engineers, ML researchers, and computational chemistry scientists, acting as a glue to bridge technical and scientific perspectives.
- Stakeholder Engagement: Act as the primary point of contact for internal stakeholders, gathering input and communicating progress effectively.
- Data-Driven Decision Making: Leverage data and feedback to make informed decisions and iterate on product features.
- Product Strategy: Translate company objectives into actionable product plans, ensuring alignment with user needs and strategic goals.
- Execution Leadership: Drive projects from concept to completion, ensuring high-quality deliverables on time and within scope.
- Risk Management: Identify potential risks in product development and proactively implement mitigation strategies.
Within 12 months, success in this role should look like:- A functionally closed AI 14 Chem loop
- Measurable performance gains from internal data
- Faster model-lab feedback cycles
- Standardized, scalable execution playbooks replacing heavy customization
- Direct contribution to major method launches by bridging scientific insight and product execution
Who you are:- 5+ years of product management experience in a technical or scientific environment
- Bachelor19s degree in Computer Science, Engineering, Chemistry, or related field (advanced degree preferred)
- Strong understanding of machine learning systems and data infrastructure
- Literacy in drug discovery, structural biology, or computational chemistry
- Proven ability to operate at the interface of ML and life sciences
- Demonstrated experience translating computational results into actionable scientific outcomes
- Experience leading complex, cross-functional initiatives
- Mindset: Passionate about innovation, problem-solving, and making a tangible impact on human health.
You are someone who:- Runs toward ambiguity rather than away from it
- Knows what you don19t know and does not bluff in technical domains
- Thinks in systems and anticipates what will not scale
- Balances strategic thinking with hands-on execution
- Is motivated by advancing methods that impact real patients
*Experience in biotech, pharma, frontier AI, or computational research environments is strongly preferred. 
