Principal Scientist, Protein Engineering
Xaira Therapeutics is hiring a Principal Scientist, Protein Engineering in South San Francisco, United States. It pays $164k-$205k a year and Level rates it ; you can apply on Xaira Therapeutics.
Level's read on this job
- Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
- The posting mentions Generative AI, Protein Design and protein engineering among its tools and skills.
- Level classes it as a senior role.
- Xaira Therapeutics has 30 other open roles on Level.
AI in this role
Lead AI-driven protein and antibody discovery campaigns, combining experimental protein engineering with computational design strategies.
About Xaira Therapeutics
Xaira is an innovative biotech startup focused on leveraging AI to transform drug discovery and development. The company is leading the development of generative AI models to design protein and antibody therapeutics, enabling the creation of medicines against historically hard-to-drug molecular targets. It is also developing foundation models for biology and disease to enable better target elucidation and patient stratification. Collectively, these technologies aim to continually enable the identification of novel therapies and to improve success in drug development. Xaira is headquartered in the San Francisco Bay Area, Seattle, and London.
About the Role
As a member of the Protein Engineering team at Xaira, you will play a critical role in the AI-driven discovery and development of protein therapeutics, integrating experimental protein engineering with computational design.
As a Principal Scientist, you will provide scientific and technical leadership from program conception through early discovery and lead optimization. You will partner closely with computational protein designers to guide AI-driven design campaigns and develop experimental strategies to evaluate designed molecules. You will engineer, produce, and characterize therapeutic proteins and antibodies, with an emphasis on optimizing functional properties and developability.
The ideal candidate will bring deep technical expertise in protein engineering, a strong understanding of antibody formats and developability metrics, and a demonstrated ability to guide scientific strategy and mentor junior staff. This is a hands-on, lab-based, on-site position with significant program leadership responsibilities.
Responsibilities
- Define the scientific strategy for one or more therapeutic programs in partnership with the Disease Biology and Therapeutic Protein Design teams. Contribute to the evaluation of novel targets, define therapeutic hypotheses and target product profiles, and develop integrated, AI-driven drug discovery strategies.
- Work closely with computational protein designers to identify relevant structural and biological features of therapeutic targets and incorporate these insights into design campaigns.
- Lead binder discovery campaigns, working closely with internal teams to prioritize screening strategies, interpret results, and identify hits.
- Collaborate with protein designers to develop and execute strategies to optimize binder affinity, specificity, and developability.
- Serve as a scientific lead in cross-functional program meetings, communicating protein engineering strategy and progress to internal stakeholders
- Design, produce, purify, and characterize therapeutic proteins, including antibodies, using chromatographic techniques and biophysical and biochemical assays..
- Maintain accurate, timely, and complete experimental records in an electronic laboratory notebook (ELN).
- Contribute to the preparation of technical reports, regulatory documentation, and scientific presentations.
- Ensure compliance with laboratory safety protocols and contribute to laboratory organization and operational efficiency.
- Stay current with advances in antibody engineering and protein design, and apply these insights to help shape the team’s scientific direction.
- Mentor and develop junior scientific staff, supporting a high-performing, collaborative team.
- Oversee CROs supporting outsourced work, ensuring data quality and timely delivery.
- Help define and prioritize the Protein Engineering team’s project portfolio in collaboration with leadership.
Qualifications
- Ph.D. in a relevant scientific discipline, with a strong foundation in molecular biology, biochemistry, structural biology, or a related field.
- At least 6 years of industry experience in biotech or biopharma R&D supporting large-molecule therapeutic programs.
- Extensive hands-on experience in protein engineering, including structural and functional characterization of antibodies and other proteins using advanced analytical methods.
- Deep expertise in antibody formats, production, purification, and characterization.
- Proficiency in developability assessments and stability studies.
- Proficiency with protein visualization and structure prediction tools, such as PyMOL and AlphaFold.
- Strong organizational, communication, and interpersonal skills.
- Ability to propose novel scientific approaches and a strong interest in exploring relevant therapeutic hypotheses.
- A proactive, self-directed approach, with excellent problem-solving and troubleshooting skills.
- Enthusiasm for working in a fast-paced, collaborative startup environment, with a demonstrated ability to promote cooperation and knowledge sharing across multidisciplinary teams.
- A strong record of scientific achievement, demonstrated by first-author publications in peer-reviewed journals or granted patents.
- Direct experience with structure prediction or machine learning tools for protein design is preferred.
- Experience collaborating with Process Development and supporting IND documentation is nice to have.
Compensation
Annual
We offer a competitive compensation and benefits package, seeking to provide an open, flexible, and friendly work environment to empower employees and provide them with a platform to develop their long-term careers. A Summary of Benefits is available for all applicants. We offer a competitive package that includes base salary, bonus, and equity. The base pay range for this position is expected to be $164,000 to $205,000 annually; however, the base pay offered may vary depending on the market, job-related knowledge, skills and capabilities, and experience.
Xaira Therapeutics an equal-opportunity employer. We believe that our strength is in our differences. Our goal to build a diverse and inclusive team began on day one, and it will never end.
TO ALL RECRUITMENT AGENCIES: Xaira Therapeutics does not accept agency resumes. Please do not forward resumes to our jobs alias or employees. Xaira Therapeutics is not responsible for any fees related to unsolicited resumes.
How we rate this
Principal Scientist, Protein Engineering at Xaira Therapeutics rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where protein engineering was part of your work. What did you do?
- Tell me about a project where computational biology was part of your work. What did you do?
- Tell me about a project where antibody discovery was part of your work. What did you do?
- Tell me about a project where drug discovery was part of your work. What did you do?
- Walk me through how you've used Generative AI in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Protein Engineering, Computational Biology, Antibody Discovery, Drug Discovery, and Generative AI. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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