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 design company, working directly with the founding team. You will advance pocket-conditioned discrete diffusion models for sequence design, operate an inference platform at scale, and close the loop with hands-on kinetics, making you one of the most end-to-end scientists on a lean core team of five to seven people.

What You'll Do

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

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

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

Run BLI and SPR end-to-end: assay design, immobilization, regeneration, referencing, dilution series, kinetic fitting, and QC.

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

Work across receptor biology, protein structure, scoring functions, and sequence design outputs.

Close the loop: take sequences from the platform, generate kinetics data, update the model, and iterate on improved sequences.

What We're Looking For

2+ years personally building or operating discrete diffusion models, protein language models (e.g. ESM, ProtT5), or structure prediction systems in a real design-make-test cycle.

Hands-on experience writing and debugging liquid-handler protocols on robotic platforms and shipping them to production.

3+ years in a GTM, solutions engineering, or customer success role in biotech or life sciences SaaS, with a track record converting free-tier users to paid tiers.

Direct, personal wet-lab experience; not limited to supervising core facilities.

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

Proficiency in Python for scripting robot methods, automation, and kinetic curve fitting.

Comfort treating protein language models and sequence design tools (e.g. RFdiffusion, BindCraft) as inputs and outputs, not black boxes.

Understanding of receptor biology, protein structure, and scoring functions sufficient to diagnose why a predicted ddG failed on a sensor.

Operator mentality: bias toward direct execution, rapid iteration, and shipping results.

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

Experience at biotech startups, accelerators, or prior exits is strongly valued.

Compensation & 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 heavy equity and deal-contingent upside. No visa sponsorship available.

Location

Hybrid, based in New York, NY. Increased on-site presence expected once internal screening instrumentation is operational, anticipated within three to six months.