Product Engineer

Job Responsibilities

• Own features end-to-end — from a rough product hypothesis to a shipped, instrumented experience scientists rely on daily.
• Work across the application layer — frontend state, API contracts, real-time data flows — making sound calls on systems that are already in use.
• Ramp quickly into live systems and make them better — building new features and surfaces while strengthening what's already there.
• Coordinate across the teams whose work feeds this surface — Machine Learning, AI Research, Data Engineering, and Platform — communicating proactively and driving clarity where requirements are still forming.
• Turn loosely-specified problems into shippable scope: prototype, get it in front of real users, iterate.
• Hold the line on reliability and user experience — these are scientific workflows where reproducibility and provenance matter.

• 5+ years of product / software engineering experience
• Strong track record in a fast-moving environment — you've shipped real products, taken ownership without being asked, and know what good looks like. You bring an entrepreneurial mindset.
• A full-stack engineer with frontend as your primary surface. You're confident with Type Script/JavaScript and React (or similar) and comfortable owning the whole stack — APIs, data flows, and the systems beneath.
• Strong product sense — you make and explain trade-offs between technical and UX considerations.
• A proactive communicator who manages dependencies across teams and surfaces issues early.
• Genuinely drawn to autonomous labs and scientific automation, and curious about the people you're building for.

Not strictly required – any of these is a meaningful plus:

• Prior experience building software in or close to a scientific environment — alongside scientists, in pharmaceutical companies, biotech, or research/clinical lab settings.
• Familiarity with instrument APIs, lab protocols, and the data-quality expectations of scientific workflows.
• Experience building AI/ML-powered product features, or working closely with ML teams to bring models into a product.
• Experience with human-in-the-loop or human–machine interaction products.