Product and Applications Lead

About the Role

Talus Bio is looking for a scientist to lead product and AI applications, joining a team full-time in our Seattle office (this is an in-person role). This person will be naturally curious, results-driven, and eager to work in a small preclinical biotech environment. You will own the path from taking a state-of-the-art drug discovery model, Ptarmigan, to a product that drug discovery teams use, pay for, and build programs around.

Ptarmigan is our structure-free model that predicts which small molecules engage which proteins across the human proteome. You will run pilot experiments with prospective pharma and biotech partners, from target and library selection through hit interpretation, and convert strong pilots into active collaborations. You will be Ptarmigan's voice with users, at conferences, events, and in partner meetings. You will bring what you learn back to the team as evidence about which use cases matter and what the product should do next.

This role suits a computational or medicinal chemist, or a chemical biologist, who has worked with customers and wants to own a product. The person in this role will play a key role in supporting our drug discovery programs targeting transcription factors implicated in cancer, working closely with a multidisciplinary team of scientists to design and analyze experiments that help improve Ptarmigan and advance lead compounds through preclinical stages.

What You Will Do

• Design and run pilot experiments with our latest models for prospective partners.
• Choose targets, libraries, and controls, agree in advance on the result that would convince the partner's chemists, and present the outcome.
• Convert successful pilots into paid plans or collaborations, working with leadership on scope and deal terms.
• Interview users and track how they use the portal and API/MCP, and where additional validation data is needed.
• Turn those interviews and the usage data into a ranked view of use cases and future experiments to run.
• Serve as alliance manager for signed collaborations.
• Run joint project meetings and steering committees, track deliverables and milestones against the contract, keep partners current on results, and find opportunities to expand the work.
• Give Ptarmigan talks and demos at scientific and machine learning conferences.
• Meet prospective users, qualify leads, and report what each audience responds strongest to.
• Plan and run events including hackathons, workshops, and benchmark challenges, including events built around agents that call our models through MCP.
• Write and maintain product materials, including application notes, case studies, demo notebooks, the website, and pitch decks.
• Own go-to-market campaigns across outreach, content, paid channels, and agent directories.
• Review and approve what our automated outreach system drafts.
• Report metrics on general awareness of our models, usage, and conduct customer interviews.

Qualifications

Required

• B.S. and 5+ years of experience in computational chemistry, medicinal chemistry, chemical biology, or a related field.
• Experience in small molecule drug discovery, including SAR, hit validation, and lead optimization.
• Customer-facing experience, such as application science, field science, or partner programs.
• Comfort working with LLMs, AI agents, APIs, and Python, and enough fluency with machine-learning models to explain their strengths and failure modes to chemists.
• Ability to present to technical audiences (eg. medicinal chemists and machine-learning researchers), and non-technical audiences (eg. business development).

Preferred

• Ph.D. and 5+ years of experience in computational chemistry, medicinal chemistry, chemical biology, or a related field, or equivalent industry experience.
• Prior experience in AI-driven drug discovery.
• Familiarity with developing and validating high-throughput biophysical and/or biochemical screening assays.
• Familiarity with developing and validating cell-based assays (e.g., cell viability, reporter assays for evaluating compound effects).