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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.
Responsibilities
- 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.
Requirements
- 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.
Skills
- Discrete diffusion models
- Protein language models
- Structure prediction systems
- AI inference stack
- Fluid-handling robotics
- Plate automation
- BLI
- SPR
- Assay design
- Immobilization
- Regeneration
- Referencing
- Dilution series
- Kinetic fitting
- QC
- Failure-mode diagnosis
- Cloud labs
- Screening instrumentation
- Receptor biology
- Protein structure
- Sequence design tools
- Python
- Scripting
- Automation
- Kinetic curve fitting
- Biochemistry
- Life sciences
- Gene editing
- Gene therapy
- Receptor trafficking
Location
- San Francisco, California, US
Work Type
- Hybrid
Experience Level
- 2+ years
Education Level
- Strong background in biology, biochemistry, or life sciences
Salary/Compensations
- $3,000 to $5,000 per month (consulting)
- $80,000 to $200,000 (full-time base salary)
Benefits
- Meaningful equity
- Deal-contingent upside