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.