Computational and Experimental Scientist

About the Role

This is a full-stack scientist role at an early-stage AI-driven protein and peptide design company, sitting directly on a lean core team of 5 to 7 and reporting to the CEO. You will own the entire design-make-test-model loop, from sequences out of the inference platform to kinetics data back in, closing that loop end-to-end rather than handing off between functions.

What You'll Do

• Improve and extend pocket-conditioned discrete diffusion models and companion folding models, including refinements, new attention heads, and hierarchical reasoning.

• Operate the AI inference stack at scale and diagnose usage patterns across signups, churn, and customer segments.

• Own fluid-handling robotics and plate automation (Hamilton, Tecan, Opentrons, or equivalent), writing and shipping reliable protocols.

• Own BLI and SPR end-to-end: assay design, immobilization, regeneration, referencing, dilution series, kinetic fitting, QC, and failure-mode diagnosis.

• Write protocols for cloud labs and manage internal screening instrumentation.

• Work the full stack across receptor biology, structure, scoring, and platform output.

• Take sequences from the platform, run kinetics, update models, and ship improved sequences.

What We're Looking For

• 2+ years building or operating discrete diffusion models, protein language models (such as ESM or ProtT5), or structure prediction systems in an active design-make-test cycle, not just academic fine-tuning.

• Personally written and debugged liquid-handler protocols on robotic platforms and shipped them to production.

• Personally fitted BLI or SPR kinetic curves end-to-end and diagnosed failure modes such as mass transport, tip avidity, nonspecific binding, aggregation, or hook effect.

• Fluent with sequence design tools (such as RFdiffusion or BindCraft) as inputs and outputs, not as black boxes.

• Able to explain why a predicted ddG failed on a sensor and trace the root cause.

• Proficient in Python or equivalent scripting for automation and kinetic curve fitting.

• Strong background in biology, biochemistry, or life sciences, with receptor biology and protein structure literacy.

• Operator mentality: resourceful, action-oriented, and comfortable executing under pressure at an early-stage company.

• Background in gene editing, gene therapy, or receptor trafficking is a plus.

• Prior experience at biotech accelerators or as an operator at a biotech startup or exit is a plus.

Compensation and Benefits

Initial consulting engagement: $3,000 to $5,000 per month. Full-time conversion: base salary of $80,000 to $200,000 depending on profile, with meaningful equity and deal-contingent upside. No visa sponsorship is available.

Location

Hybrid, with increased on-site presence expected once internal screening infrastructure is established (roughly 3 to 6 months out). Primary location is San Francisco, California, US.