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
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Improve and extend pocket-conditioned discrete diffusion models and companion folding models, including refinements, new attention heads, and hierarchical reasoning.
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Operate the AI inference stack at scale and diagnose usage patterns across signups, churn, and customer segments.
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Own fluid-handling robotics and plate automation (Hamilton, Tecan, Opentrons, or equivalent), writing and shipping reliable protocols.
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Own BLI and SPR end-to-end: assay design, immobilization, regeneration, referencing, dilution series, kinetic fitting, QC, and failure-mode diagnosis.
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Write protocols for cloud labs and manage internal screening instrumentation.
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Work the full stack across receptor biology, structure, scoring, and platform output.
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Take sequences from the platform, run kinetics, update models, and ship improved sequences.
What We’re Looking For
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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.
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Personally written and debugged liquid-handler protocols on robotic platforms and shipped them to production.
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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.
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Fluent with sequence design tools (such as RFdiffusion or BindCraft) as inputs and outputs, not as black boxes.
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Able to explain why a predicted ddG failed on a sensor and trace the root cause.
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Proficient in Python or equivalent scripting for automation and kinetic curve fitting.
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Strong background in biology, biochemistry, or life sciences, with receptor biology and protein structure literacy.
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Operator mentality: resourceful, action-oriented, and comfortable executing under pressure at an early-stage company.
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Background in gene editing, gene therapy, or receptor trafficking is a plus.
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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.