BigHat Biosciences

ML-guided antibody discovery and engineering

Scientist 1 - Display Technologies

Translational Research & Bench BiologyConfirmed live in the last 24 hours

Full-timeSan Mateo, CA, USANo salary listed

About the Job

Requirements

Ph.D. in Biochemistry, Structural Biology, Protein Engineering, or a related field with up to 2 years of industry research experience, or an M.S. in a related field with 6 or more years of relevant industry research experience.
Experience with display technologies, including library design and selection strategies.
Knowledge of antibody biology and protein expression.
Strength in general molecular biology techniques, including polymerase chain reaction, molecular cloning, and gel electrophoresis.
Ability to follow protocols carefully, troubleshoot experiments, and maintain detailed laboratory records.
Effective time management and clear written and verbal communication skills.
Collaborative, team-oriented mindset and eagerness to learn in a fast-paced startup environment.
Scientific track record and publication record.
Strong data analysis and interpretation skills.

Responsibilities

Support research and development projects advancing display technologies to expand antibody discovery capabilities and feed machine-learning models.
Lead the construction and quality control of phage display libraries across diverse antibody formats, including VHH, single-chain variable fragments, and Fab, using standard molecular biology techniques such as polymerase chain reaction, molecular cloning, and electroporation.
Execute standard phage display selections to engineer antibodies with desired therapeutic properties.
Express, purify, and characterize display-derived molecules using assays including enzyme-linked immunosorbent assay, flow cytometry, biolayer interferometry/surface plasmon resonance, and capillary electrophoresis-sodium dodecyl sulfate.
Analyze selection results and contribute to discussions on next steps and future experimental design.
Collaborate with Data Science and Machine Learning teams to inform library design and interpret selection data for model training.
Collaborate with leadership and cross-functional team members to link strategy, research, and development.
Present experimental plans and results during regular meetings with the manager and science teams.
Communicate results regularly to key decision makers, the broader team, and external partners.
Maintain thorough records using laboratory information management systems and electronic notebooks.

Desired Qualifications

Experience with flow cytometry, enzyme-linked immunosorbent assay, or other binding assays.
Familiarity with next-generation sequencing or automation platforms.
Experience working with agentic software tools.

The role: BigHat Biosciences is seeking a curious and motivated Scientist 1 to join the Display Technologies team. This individual will contribute to BigHat’s antibody discovery and engineering efforts and help generate high-quality data to improve BigHat’s machine learning models using display technologies, working closely with senior scientists and cross-functional teams.

Support R&D projects advancing display technologies at BigHat that expands BigHat's antibody discovery capabilities and feed its ML models.
Lead in the construction and QC of phage display libraries across diverse antibody formats (VHH, scFv, Fab) using standard molecular biology techniques (PCR, molecular cloning, electroporation).
Execute standard phage display selections to engineer antibodies with desired therapeutic properties.
Express, purify, and characterize display-derived molecules using assays including ELISA, flow cytometry, BLI/SPR, CE-SDS, and others.
Analyze selection results and contribute to discussions on next steps and future experimental design.
Collaborate with BigHat’s Data Science and Machine Learning teams to inform library design and interpret selection data for model training.
Collaborate closely with BigHat leadership and other cross-functional team members to ensure a tight linkage between strategy, research, and development components.
Present experimental plans and results during regular meetings with manager and science teams.
Effectively and regularly communicate results to key decision makers, the broader BigHat team, and to external partners.
Maintain thorough records using LIMS and electronic notebooks.

Ph.D. in Biochemistry, Structural Biology, Protein Engineering, or a related field, with up to 2 years of industry research experience; or an M.S. in a related field with 6+ years of relevant industry research experience.
Experience with display technologies, including library design and selection strategies.
Knowledge of antibody biology and protein expression.
Strength in general molecular biology techniques (PCR, molecular cloning, gel electrophoresis).
Nice-to-haves include experience with flow cytometry, ELISA, or other binding assays, and familiarity with NGS or automation platforms.
Demonstrated ability to follow protocols carefully, troubleshoot experiments, and maintain detailed lab records.
Effective time management and clear written and verbal communication skills.
Collaborative, team-oriented mindset with an eagerness to learn in a fast-paced startup environment.
Scientific track record and publication record.
Strong data analysis and interpretation skills. Experience working with agentic software tools is a plus.

About the Company

BigHat Biosciences

ML-guided antibody discovery and engineering

BigHat Biosciences designs and develops antibody therapies by combining machine learning with synthetic biology. Its Milliner platform integrates a wet-lab workflow with AI to discover, engineer, and fully characterize hundreds of recombinant antibodies each week, enabling rapid development of safer and more effective treatments for hard-to-treat diseases such as certain infections and cancers. Unlike traditional antibody discovery, Milliner provides end-to-end screening, purification, and characterization in a high-throughput, weekly workflow, speeding up the path from concept to candidate. The company differentiates itself through its integrated platform that couples ML-driven design with synthetic biology experimentation, a focus on challenging diseases, and a culture that emphasizes work-life balance. Its goal is to deliver faster, safer antibody therapies to patients by expanding access to rapid discovery and engineering capabilities for healthcare providers and patients alike.

Company size: 51-200
Company stage: Series C
Total funding: $174.3M
Headquarters: San Mateo, CA
Founded: 2019

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