Product Management Lead
Genesis Molecular AI is pioneering a transformative approach to drug discovery by leveraging state-of-the-art machine learning, computational chemistry, and biology. Our mission is to accelerate the development of life-changing therapies by merging cutting-edge technology with innovative science. We are building a world-class team to drive forward the future of drug discovery.
About the Team:
We are a mission-driven group of scientists and engineers using cutting-edge computational methods to discover drugs for unmet medical needs. Our work spans machine learning, computational chemistry, biology, and wet lab experimentation, all operating within a high-trust, deeply technical environment.
You’ll join a team with extremely strong ML talent that is actively pushing the frontier of AI in drug discovery. We are refining how experimental data, biological context, and computational models integrate into a seamless feedback loop.
You will help reshape how data flows from experiment → infrastructure → model → decision and increase the speed and scale at which we learn.
The Role
This 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 ↔ 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 you’ll 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 ↔ 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
Bachelor’s 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 don’t 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.
About Genesis Molecular AI
Genesis Molecular AI is pioneering foundation models for molecular AI to unlock a new era of drug design and development. Our generative and predictive AI platform, GEMS (Genesis Exploration of Molecular Space), integrates AI and physics into industry-leading models to generate and optimize drug molecules, including the breakthrough generative diffusion model Pearl for structure prediction. Genesis is backed by premier AI and life science investors, including a16z, NVIDIA, Rock Springs Capital, Menlo Ventures, T. Rowe Price, Fidelity, and Radical Ventures. Genesis has also signed category-leading AI-pharma deals, the most recent of which was a significant expansion with Incyte (see coverage in Forbes and GEN) with a total potential deal value of several billion dollars.
Genesis is headquartered in San Mateo, CA, with a fully integrated laboratory in San Diego. We are proud to be an inclusive workplace and an Equal Opportunity Employer.