Senior Scientist, Protein Engineering
Job description
About the role
The Senior Scientist, Protein Engineering will shape next-generation protein therapeutics within a biotech startup that leverages AI to design protein and antibody medicines. This role operates on-site in South San Francisco, California, and reports to the Protein Engineering team. The position focuses on translating AI-driven design concepts into experimentally validated protein constructs with optimized properties. The hire will own the end-to-end protein engineering strategy for therapeutic programs from initial design through developability assessment. They will directly influence the generation of high-affinity and specific candidates that advance toward IND enabling studies. Success in this role requires close collaboration with cross-functional partners to ensure scientific decisions align with project timelines and regulatory expectations. The individual will mentor junior scientists and help establish rigorous protein engineering standards that scale with the growing team.
Key facts
What you'll do
Design and execute protein engineering approaches to improve affinity, specificity, format, and developability for therapeutic proteins and antibodies.
Coordinate binder discovery campaigns by setting screening priorities, interpreting biochemical and biophysical data, and advancing lead molecules in partnership with internal discovery teams.
Present findings at internal protein engineering meetings and cross-functional gatherings to align stakeholders and communicate technical progress clearly.
Prepare technical reports, regulatory documentation, and scientific presentations to support project milestones, IND enabling packages, and external submissions.
Track advances in antibody engineering and protein design methodologies to align internal methods and strategies with current scientific standards and best practices.
Mentor junior scientific staff, providing hands-on guidance to build a high-performing, collaborative team capable of delivering complex protein programs on schedule.
Utilize protein visualization and structural modeling tools such as PyMOL and AlphaFold to interpret structural features and guide rational design decisions.
Prefer experience with protein-protein interaction assays using biophysical techniques such as Surface Plasmon Resonance (SPR) or Biolayer Interferometry (BLI) for potency and affinity assessment.
Implement developability assessments and stability studies as part of the decision-making framework for molecule progression and selection.
Collaborate with Process Development and Chemistry teams to ensure that protein constructs are compatible with manufacturing and formulation constraints.
Contribute to the generation of IND-enabling documentation packages in partnership with regulatory and translational medicine teams.
Act as a proactive problem solver, diagnosing complex biological challenges and devising experiments to test therapeutic hypotheses efficiently.
Thrive in a fast-paced, iterative startup environment by organizing workstreams, prioritizing multiple projects, and maintaining clear documentation.
Foster knowledge sharing across multidisciplinary teams by presenting internal talks, writing summaries, and participating in group learning sessions.
Maintain a strong record of scientific achievement, evidenced by first-author publications in peer-reviewed journals, posters, or granted patents related to protein therapeutics.
Requirements
Hold a Ph.D. in a relevant scientific discipline with a strong foundation in molecular biology, biochemistry, structural biology, or a related field.
Bring 4+ years of industry experience in biotech or biopharma R&D focused on large molecule therapeutic programs.
Demonstrate extensive hands-on experience in protein engineering, including structural and functional characterization of antibodies and proteins with analytical methods such as SPR, BLI, and biophysical assays.
Show deep expertise in antibody formats, production in cellular systems, purification strategies, and comprehensive characterization of therapeutic candidates.
Apply developability assessments, stability studies, and biochemical profiling to guide decision-making for molecule progression and optimization.
Use protein visualization and structural modeling software such as PyMOL and AlphaFold to support design, analysis, and communication of structural insights.
Prefer experience with protein-protein interaction assays using techniques such as SPR or BLI to quantify binding kinetics and affinity.
Prefer experience working with Process Development teams and contributing to IND-enabling documentation packages that support regulatory filings.
Exhibit strong organizational, communication, and interpersonal skills to operate effectively in a cross-functional setting with diverse stakeholders.
Propose novel scientific approaches and show a genuine interest in testing therapeutic hypotheses in a collaborative context with internal and external partners.
Act as a proactive self-starter with excellent problem-solving and troubleshooting capabilities for complex biological challenges inherent in protein drug discovery.
Thrive in a fast-paced, collaborative startup environment and foster cooperation and knowledge sharing across multidisciplinary teams spanning biology, chemistry, and engineering.
Maintain a strong record of scientific achievement, evidenced by first-author publications in peer-reviewed journals or granted patents that demonstrate innovation in protein engineering.
Nice to have
Experience interacting with external CRO partners and managing outsourced workflows to ensure timely delivery of high-quality data.
Familiarity with AI-driven protein design tools and data pipelines to leverage machine learning outputs for experimental validation.
Practical notes
This is an on-site role based in South San Francisco, California.
Travel may be required for collaboration, scientific conferences, or CRO oversight.
Typical interview steps for engineering roles usually start with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and provide candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.