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Computational Antibody Design Jobs (NOW HIRING)

... computational professional development and collaboration. The LAUNCHPAD team works closely with academic partners to design experimental strategies to identify promising antibody 'hits' as well as ...

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Computational Antibody Design information

What is computational antibody design?

Computational antibody design is the process of using computer algorithms and modeling techniques to create or optimize antibodies for specific targets. This field combines structural biology, bioinformatics, and artificial intelligence to predict how antibodies will interact with antigens, allowing researchers to design antibodies with improved affinity, specificity, and stability. Computational approaches can accelerate the development of therapeutic antibodies and reduce the need for extensive laboratory experimentation.

What are the key skills and qualifications needed to thrive in computational antibody design?

To thrive in Computational Antibody Design, you need a strong background in molecular biology, bioinformatics, structural biology, and typically a graduate degree in a relevant field. Proficiency with computational modeling tools (such as Rosetta, PyMOL, or MOE), programming languages (like Python or R), and familiarity with antibody databases is essential. Strong problem-solving abilities, attention to detail, and effective communication are important soft skills for collaborating with interdisciplinary teams. These skills enable the successful design and optimization of antibodies, accelerating therapeutic discovery and innovation.

What are some common challenges faced in a computational antibody design role, and how can they be addressed?

A common challenge in Computational Antibody Design is integrating large and complex biological datasets to accurately predict antibody-antigen interactions. Additionally, balancing computational modeling with experimental validation can be demanding due to the iterative nature of the design process. Collaboration with wet-lab scientists and bioinformaticians is crucial for refining models and validating hypotheses. Staying current with advances in machine learning and structural biology also helps address these challenges and drive innovative solutions.

What is the difference between Computational Antibody Design vs Protein Engineer?

AspectComputational Antibody DesignProtein Engineer
Required CredentialsAdvanced degrees in bioinformatics, computational biology, or related fieldsDegrees in biochemistry, molecular biology, or related fields
Work EnvironmentResearch labs, biotech companies, academia, focusing on in silico modelingLaboratories, biotech firms, industry, involving both lab work and computational tasks
Industry UsagePrimarily in pharmaceutical and biotech sectors for antibody developmentBroader industry use including enzyme design, protein optimization, and therapeutic development

Computational Antibody Design focuses on using computational methods to create and optimize antibodies, mainly in silico. Protein Engineers work on designing, modifying, and optimizing various proteins, including enzymes and therapeutic proteins, often combining lab and computational work. While both roles require strong backgrounds in biology and computational skills, their specific focus and applications differ within the biotech industry.

What other helpful pages are available for Computational Antibody Design?

Other pages related to Computational Antibody Design:

Infographic showing various Computational Antibody Design job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 87% Full Time, 9% Part Time, and 3% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.

Computational Protein Research Associate (Contract)

Tustin, CA • On-site

Zymo Research
Biotechnology Research and Development • 51 - 200 employees

$75K - $85K/yr

Other

Medical, Dental, Vision, Life, Retirement

Posted 9 days ago


Job description

Zymo Research is seeking a highly motivated and innovative Computational Protein Research Associate to join our multidisciplinary research team focused on protein engineering, enzyme discovery, antibody design, and AI-driven molecular modeling.

The successful candidate will work at the intersection of computational biology, machine learning, structural biology, and protein engineering. Key responsibilities will include developing scalable protein sequence–structure–function analysis platforms, building predictive AI/ML models, and identifying or designing proteins with desired biological properties. This role offers the opportunity to contribute to cutting‑edge platform technologies in enzymes, antibodies, synthetic biology, and molecular diagnostics.

Essential Duties and Responsibilities
  • Apply protein language models and other machine‑learning approaches, including model fine‑tuning, to analyze protein sequences, predict molecular properties, and annotate protein functions.
  • Use structural modeling and simulation tools, including AlphaFold 3, Rosetta, and molecular dynamics simulations, to investigate protein structures, conformational stability, molecular interactions, and functional mechanisms.
  • Apply AI/ML methods to de novo protein design, protein optimization, enzyme engineering, antibody design, and functional prediction.
  • Perform computational screening and prioritization of candidate proteins for experimental validation.
  • Collaborate closely with wet‑lab scientists to design experiments, interpret results, and iteratively improve engineered proteins.
  • Contribute to active‑learning‑driven Design–Build–Test–Learn cycles for protein engineering and optimization.
  • Present research findings internally and externally through reports, publications, and scientific conferences.
  • Contribute to intellectual property generation and platform development.
Education and Experience
  • Bachelor’s degree or higher in computational biology, bioinformatics, structural biology, computer science, biophysics, protein engineering, or related field.
  • Strong foundation in computational protein science or structural biology.
  • Experience applying machine‑learning or deep‑learning methods to biological sequence, structure, or functional data.
  • Proficiency in Python and commonly used scientific computing and machine‑learning libraries.
  • Experience with protein language models, structure‑based modeling, or molecular simulation is highly desirable.
  • Strong analytical, problem‑solving, and communication skills.
  • Ability to work independently while contributing effectively within a multidisciplinary research environment.
Compensation, Benefits, and Perks

The estimated base compensation range for this position is $75,000-85,000 at the time of posting. Actual compensation details will be provided in writing at the time of offer, if applicable, and are based on several factors we believe fairly and accurately reflect the role, including geographic location, experience, knowledge, skills, abilities, and other job‑permitted factors.

Zymo Research also offers competitive benefits and perks including:

  • Medical, Dental, Vision, and Life Insurance Coverages
  • EAP Sessions
  • Generous 401(K) with matching
  • Employee Referral Bonus
  • Complimentary fruit, snacks, and beverages
  • Complimentary catered lunches on Thursdays
  • Complimentary EV Charging
  • Cell phone stipend
Equal Employment Opportunity Employer

Zymo Research welcomes candidates of all backgrounds. These include sex, age, color, race, religion, marital status, national origin, ancestry, sexual orientation, gender, gender identity, gender expression, physical & mental disability, medical condition, genetic information, military and veteran status, or any other protected status as defined by federal, state, or local law.

Location

Onsite – Zymo Research – 2961 Dow Ave, Tustin, CA 92780

Disclaimer

At Zymo, we take the integrity of our hiring process seriously. Please be aware of fraudulent recruitment activities that may use our name to deceive job seekers. We will never ask for payment, sensitive personal information, or financial details during the recruitment process.

All legitimate communications will come from an official Zymo or TriNet Hiring email address. If you are contacted by anyone claiming to represent us using a free email service (e.g., Gmail, Yahoo, Hotmail) or asking for payment, please treat this as fraudulent and report it immediately to jobs@zymoresearch.com.

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