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About the Role
Join a lean team at an early-stage AI-driven biotech company, owning computational and experimental work across protein design, laboratory testing, and model improvement. You will close the design-make-test loop, translating sequences into kinetics data and using results to improve the next round of designs.
Responsibilities
- Improve discrete diffusion and companion folding models for peptide and protein design, including architecture refinements and hierarchical reasoning.
- Operate and refine an inference platform, investigate usage patterns, and help scientists apply its outputs.
- Write, debug, and maintain production protocols for liquid-handling robots and plate automation.
- Run BLI and SPR workflows end to end, from assay design and immobilization through kinetic fitting and quality control.
- Develop clear protocols for external laboratories and operate internal screening instrumentation.
- Connect receptor biology, protein structure, scoring, and experimental results to identify failure modes and improve designs.
- Support scientific platform users and customer workflows, including personalized technical outreach and navigating biotech and pharma purchasing processes.
Requirements
- Typically 3 to 11 years of relevant experience, including experience in biotech or life sciences software and customer-facing technical or commercial work.
- Hands-on wet-lab experience and a background in biology, biochemistry, or a related life science.
- Experience building or operating protein design, protein language, discrete diffusion, or structure prediction models in an experimental design-make-test cycle.
- Practical use of tools such as ESM, ProtT5, RFdiffusion, BindCraft, or equivalents.
- Personally developed and debugged production methods for Hamilton, Tecan, Opentrons, or equivalent liquid-handling platforms.
- End-to-end BLI or SPR kinetic fitting experience, including diagnosing assay and measurement failure modes.
- Python or equivalent scripting skills for automation and kinetic data analysis, plus an understanding of receptor biology, protein structure, and scoring.
- Experience operating inference systems at scale, improving model architectures, and working across experimental biology, bioinformatics, and machine learning.
Skills
- Protein design
- Computational modeling
- Laboratory testing
- Model improvement
- Discrete diffusion models
- Folding models
- Peptide design
- Protein design
- Architecture refinement
- Hierarchical reasoning
- Inference platform operation
- Liquid-handling robot protocols
- Plate automation protocols
- BLI workflows
- SPR workflows
- Assay design
- Immobilization
- Kinetic fitting
- Quality control
- Receptor biology
- Protein structure
- Scoring
- Python scripting
- Data analysis
- Experimental biology
- Bioinformatics
- Machine learning
Location
- New York, United States
Work Type
- Hybrid
Experience Level
- 3 to 11 years
Education Level
- Biology
- Biochemistry
- Related life science
Salary/Compensations
- $80,000 to $200,000 annually
- $3,000 to $5,000 per month (Consulting)
Benefits
- Equity
- Deal-contingent upside
About the Company
- Early-stage AI-driven biotech company