Computational and Experimental Scientist

About the Role

This role owns the full design-make-test-model loop at an early-stage AI-driven protein and peptide engineering company: you will run and improve pocket-conditioned discrete diffusion models for sequence design and personally execute the binding kinetics that close the loop. You will sit on a lean core team reporting directly to the CEO, making this one of the highest-leverage scientific roles at the company.

What You'll Do

• Improve and extend proprietary diffusion and companion folding models with architectural refinements, new attention heads, and hierarchical reasoning.

• Operate an ML inference platform at scale and diagnose usage patterns across signups, churn, and customer segments.

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

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

• Write detailed cloud-lab protocols and manage internal screening instrumentation.

• Work the full stack from receptor biology and protein structure through scoring functions to platform outputs that scientists will actually use.

• Close design loops: take sequences from the platform, run kinetics, update the model, 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 a real make-test-model cycle, not academic papers or public fine-tunes.

• Personally written and debugged liquid-handler protocols on robotic platforms and shipped them to production, not supervised a core facility.

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

• Proficiency coding robot methods and analyzing kinetic data in Python or equivalent scripting.

• Demonstrated ability to close a full loop: design sequences, synthesize or express, measure kinetics, update the model, iterate.

• Comfortable treating protein language models and sequence design tools (such as RFdiffusion or BindCraft equivalents) as inputs and outputs, not black boxes.

• Strong background in biology, biochemistry, or a closely related life-sciences field.

• Operator mentality: resourceful, action-oriented, comfortable executing at odd hours to have data ready the next day.

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

• Prior experience at biotech accelerators or early-stage biotech startups is a plus.

Compensation and Benefits

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

Location

Hybrid in New York, NY. On-site presence will increase once internal screening instrumentation is operational (expected within 3 to 6 months).