Computational and Experimental Scientist

About the Role

Join a lean team at an early-stage AI and biotech company, owning computational and experimental work across protein design and an inference platform. You will connect sequence design, laboratory testing, and model improvement in an end-to-end design-make-test loop.

What You'll Do

• Improve and extend discrete diffusion and protein folding models, including model refinements, attention mechanisms, and hierarchical reasoning.
• Operate and scale an inference platform, and analyze usage patterns such as signups, churn, and customer segments.
• Develop, debug, and maintain production protocols for liquid-handling robots and plate automation.
• Design and run BLI and SPR assays, including immobilization, regeneration, referencing, dilution series, kinetic fitting, and quality control.
• Write precise protocols for external laboratory partners and manage screening instrumentation.
• Connect receptor biology, protein structure, scoring, and model outputs to practical scientific workflows.
• Use experimental kinetics data to update models and improve designed sequences.

What We're Looking For

• Typically 3 to 11 years of relevant experience, including experience in biotech or scientific software roles such as solutions engineering, customer success, or a hybrid operator position.
• Hands-on experience building or operating protein design, discrete diffusion, protein language, or structure prediction systems within an experimental design-make-test cycle.
• Practical experience with tools such as ESM, ProtT5, RFdiffusion, BindCraft, or equivalent approaches.
• Direct experience writing and debugging production methods for Hamilton, Tecan, Opentrons, or equivalent liquid-handling systems.
• Hands-on BLI or SPR kinetic curve fitting, assay design, and diagnosis of assay failure modes.
• Ability to code robot methods and analyze kinetic data using Python or equivalent scripting tools.
• Understanding of biology, biochemistry, receptor biology, protein structure, and scoring functions, with the ability to investigate when predictions do not match experimental results.
• Experience completing a full loop from sequence design through synthesis or expression and kinetics measurement to model updates and iteration.
• Comfort with scientific customer engagement, including personalized outbound communication and navigating buying processes across organizations of different sizes.