Description review
Computational and Experimental Scientist
Clera · Remote - New York · back to the listing
HR standards
44/100
poor
Title ↔ description
57/100
needs work
Reads as
Unclear
no confident match
What this role officially is
data scientist — ESCO, the EU occupation classification
Data scientists find and interpret rich data sources, manage large amounts of data, merge data sources, ensure consistency of data-sets, and create visualisations to aid in understanding data. They build mathematical models using data, present and communicate data insights and findings to specialists and scientists in their team and if required, to a non-expert audience, and recommend ways to apply the data.
Also known as: data scientists, data engineer, research data scientist, data expert, data research scientist
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What the listing never says
- 16 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
The listing, marked up
About the Role
Join a lean team at an early-stage AI and biotech company, owning computational and experimental work across protein design and an inference platform. You will connect sequence design, laboratory testing, and model improvement in an end-to-end design-make-test loop.
What You'll Do
• Improve and extend discrete diffusion and protein folding models, including model refinements, attention mechanisms, and hierarchical reasoning.
• Operate and scale an inference platform, and analyze usage patterns such as signups, churn, and customer segments.
• Develop, debug, and maintain production protocols for liquid-handling robots and plate automation.
• Design and run BLI and SPR assays, including immobilization, regeneration, referencing, dilution series, kinetic fitting, and quality control.
• Write precise protocols for external laboratory partners and manage screening instrumentation.
• Connect receptor biology, protein structure, scoring, and model outputs to practical scientific workflows.
• Use experimental kinetics data to update models and improve designed sequences.
What We're Looking For
• Typically 3 to 11 years1 of relevant experience, including experience in biotech or scientific software roles such as solutions engineering, customer success, or a hybrid operator position.
• Hands-on experience building or operating protein design, discrete diffusion, protein language, or structure prediction systems within an experimental design-make-test cycle.
• Practical experience with tools such as ESM, ProtT5, RFdiffusion, BindCraft, or equivalent approaches.
• Direct experience writing and debugging production methods for Hamilton, Tecan, Opentrons, or equivalent liquid-handling systems.
• Hands-on BLI or SPR kinetic curve fitting, assay design, and diagnosis of assay failure modes.
• Ability to code robot methods and analyze kinetic data using Python or equivalent scripting tools.
• Understanding of biology, biochemistry, receptor biology, protein structure, and scoring functions, with the ability to investigate when predictions do not match experimental results.
• Experience completing a full loop from sequence design through synthesis or expression and kinetics measurement to model updates and iteration.
• Comfort with scientific customer engagement, including personalized outbound communication and navigating buying processes across organizations of different sizes.
Compensation & Benefits
Consulting engagement: $3,000 to $5,000 per month. Full-time conversion base salary: $80,000 to $200,000 annually, depending on experience, with equity and deal-contingent upside. Visa sponsorship is not available.
Location
Hybrid in New York, United States, with increased on-site presence expected as internal screening operations are established.
Join a lean team at an early-stage AI and biotech company, owning computational and experimental work across protein design and an inference platform. You will connect sequence design, laboratory testing, and model improvement in an end-to-end design-make-test loop.
What You'll Do
• Improve and extend discrete diffusion and protein folding models, including model refinements, attention mechanisms, and hierarchical reasoning.
• Operate and scale an inference platform, and analyze usage patterns such as signups, churn, and customer segments.
• Develop, debug, and maintain production protocols for liquid-handling robots and plate automation.
• Design and run BLI and SPR assays, including immobilization, regeneration, referencing, dilution series, kinetic fitting, and quality control.
• Write precise protocols for external laboratory partners and manage screening instrumentation.
• Connect receptor biology, protein structure, scoring, and model outputs to practical scientific workflows.
• Use experimental kinetics data to update models and improve designed sequences.
What We're Looking For
• Typically 3 to 11 years1 of relevant experience, including experience in biotech or scientific software roles such as solutions engineering, customer success, or a hybrid operator position.
• Hands-on experience building or operating protein design, discrete diffusion, protein language, or structure prediction systems within an experimental design-make-test cycle.
• Practical experience with tools such as ESM, ProtT5, RFdiffusion, BindCraft, or equivalent approaches.
• Direct experience writing and debugging production methods for Hamilton, Tecan, Opentrons, or equivalent liquid-handling systems.
• Hands-on BLI or SPR kinetic curve fitting, assay design, and diagnosis of assay failure modes.
• Ability to code robot methods and analyze kinetic data using Python or equivalent scripting tools.
• Understanding of biology, biochemistry, receptor biology, protein structure, and scoring functions, with the ability to investigate when predictions do not match experimental results.
• Experience completing a full loop from sequence design through synthesis or expression and kinetics measurement to model updates and iteration.
• Comfort with scientific customer engagement, including personalized outbound communication and navigating buying processes across organizations of different sizes.
Compensation & Benefits
Consulting engagement: $3,000 to $5,000 per month. Full-time conversion base salary: $80,000 to $200,000 annually, depending on experience, with equity and deal-contingent upside. Visa sponsorship is not available.
Location
Hybrid in New York, United States, with increased on-site presence expected as internal screening operations are established.