BigHat Biosciences

ML-guided antibody discovery and engineering

Scientist 2 - Developability

Bioprocess & BiomanufacturingConfirmed live in the last 24 hours

Full-timeSan Mateo, CA, USANo salary listed

About the Job

Requirements

A PhD in Biochemistry or a relevant discipline with at least three years of industry experience; exceptional candidates without a PhD but with deep bench experience will be considered.
Strong background in biophysical and analytical characterization of antibodies and antibody fragments, including size-exclusion chromatography (SEC), hydrophobic interaction chromatography (HIC), capillary electrophoresis sodium dodecyl sulfate (CE-SDS), dynamic light scattering (DLS), differential scanning fluorimetry (DSF), enzyme-linked immunosorbent assay (ELISA), and liquid chromatography–mass spectrometry (LC-MS), including method development and optimization.
Track record of translating complex data into clear results and recommended actions.
Experience with protein structures and antibody engineering.
Ability and enthusiasm to learn and onboard new technologies to expand capabilities and improve work quality, timelines, throughput, or cost.

Responsibilities

Lead developability research and development projects by designing, optimizing, and executing high-throughput developability assays to expand the company's toolkit.
Drive developability assessments of purity, stability, tolerability, and biophysical properties across programs, with careful data interpretation and organization.
Support late-stage characterization and investigational new drug (IND)-enabling studies, including formulation screening, stress testing, peptide mapping, and simulated administration studies, to rank and advance lead candidates.
Serve as the developability lead within project teams and guide experimental strategy to address liabilities and meet program milestones.
Work cross-functionally to automate and train colleagues on new and existing methods, build quality control (QC) frameworks to monitor assay performance, and adapt workflows to existing laboratory information management system (LIMS) infrastructure.
Independently analyze data, interpret scientific findings, propose next steps, and communicate findings across the organization.

Desired Qualifications

Prior experience working in a dynamic startup environment.
Prior experience working as a project lead.
Prior experience working with automation.

The Role: BigHat Biosciences is looking for a hands-on, bench-driven scientist to drive antibody developability assessments that advance our Milliner Platform for AI-designed therapeutics. This is a primarily lab-based role where you'll spend the majority of your time at the bench executing and innovating on our developability workflows.

*At BigHat we believe in titles that commensurate with skill set, relative organizational impact, and value contribution; more experienced candidates are encouraged to apply, with the understanding that

responsibilities and title would adjust as appropriate.

Lead developability R&D projects: design, optimize, and execute high throughput developability assays to expand BigHat’s toolkit.
Drive developability assessments (purity, stability, tolerability, biophysical properties) across programs, with strong attention to data interpretation and organization.
Support late-stage characterization and IND-enabling studies, including formulation screening, stress testing, peptide mapping, and simulated administration studies, to rank and advance lead candidates.
Serve as the Developability lead within project teams, guiding experimental strategy to address liabilities and hit program milestones.
Work cross-functionally to automate and train colleagues on new and existing methods, build QC frameworks for monitoring assay performance, and adapt workflows to existing LIMS infrastructure.
Proactively and independently analyze data, interpret scientific findings, propose next steps, and broadcast findings clearly across the organization.

PhD in Biochemistry or relevant discipline with 3+ year(s) of industry experience; exceptional non-PhD candidates with deep bench experience will be considered.
Strong background in biophysical and analytical characterization of antibodies and antibody fragments: SEC, HIC, CE-SDS, DLS, DSF, ELISA, LC-MS, including method development and optimization.
Track record of translating complex data into clear results and recommended actions.
Experience with protein structures and antibody engineering.
Enthusiastic about learning and onboarding new technologies to expand capabilities and improve work quality, timelines, throughput or cost.
Nice-to-haves include prior experience working in a dynamic start-up environment, working as a project lead, and working with automation.

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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