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Computational Antibody Design Jobs (NOW HIRING)

... design and hit‑to‑lead optimization -- executing display screens on ML‑designed variants and leveraging computational models to inform display strategies. * Partner closely with the Antibody ...

Computational Protein Engineer

Rockville, MD · On-site

$113K - $134K/yr

My client seeks a Computational Protein Engineer to build and lead hands-on experimental programs ... Lead experimental design and execution of antibody and protein engineering programs including ...

$82K - $97K/yr

My client seeks a Computational Protein Engineer to build and lead hands-on experimental programs ... Lead experimental design and execution of antibody and protein engineering programs including ...

Computational Biologist

Medford, MA · On-site

$74K - $111K/yr

Experimental design and biostatistics Collaborate with scientists before data collection to define ... and antibody-staining workflows. Participate in project meetings and help translate analytical ...

... silico computational research, and partner with experimental collaborators in a lab-in-the-loop ... Active research areas include protein and antibody design, novel AI approaches to molecular ...

... silico computational research, and partner with experimental collaborators in a lab-in-the-loop ... Active research areas include protein and antibody design, novel AI approaches to molecular ...

Showing results 41-60

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, 89% Full Time, 7% Part Time, and 3% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution.

Principal Associate Scientst-Scientist-Display Platforms and Directed Evolution

On-site

Prellis Biologics, Inc.
Biotechnology Research and Development • 11 - 50 employees

Other

Medical, Dental, Vision, Life, Retirement

Posted 25 days ago


Job description

Principal Associate Scientst-Scientist-Display Platforms and Directed Evolution

Full Time

Antibody Discovery

The heart of Prellis Bio’s strategy is the combination of novel, cutting-edge methods in machine learning, biology at scale, and next-gen antibody discovery that address long-standing industry-wide problems in the drug development pipeline. Prellis Biologics uses proprietary technology to 3D print human lymph node organoids, enabling the rapid & diverse discovery of human antibody therapeutic candidates for a range of applications. To drive this forward, we are assembling an incredible team of discovery biologists, computational scientists, and protein scientists who want to make a difference to this important problem.

Position Summary

We are seeking a Principal Associate Scientist / Scientist — Display Platforms & Directed Evolution to join our dynamic team in support of our antibody engineering effort. You will lead discovery and enrichment of antibody hits through display platforms and directed evolution, and execute affinity-maturation campaigns to advance lead molecules. You will be responsible for:

  • Leading display and directed evolution campaigns — from library construction through screening, selection, and hit characterization — to drive discovery, enrichment, and affinity maturation of antibody candidates.
  • Expanding our display platform portfolio — deploying new display formats (yeast, phage, mammalian, ribosome), screening strategies for challenging targets, and diversity library approaches to enable next-generation antibody discovery.
  • Screening AI/ML-designed variants at scale — executing display campaigns that test ML-designed antibody variants and generate high-quality datasets to inform Prellis’s next-generation antibody design models, in close partnership with the Data Science team.

This role sits within a highly cross-functional High Throughput Discovery group, with significant opportunities to grow and take on new responsibilities. The ideal candidate will thrive in a dynamic, hands‑on environment and play a critical role in delivering life‑changing therapeutics.

Responsibilities

  • Scope, build, deploy, and execute display and directed evolution campaigns (yeast, phage, mammalian, ribosome, or comparable) for hit discovery, enrichment, and affinity maturation of lead molecules — both independently and collaboratively with up‑and downstream teams to achieve program goals.
  • Lead the design and sourcing of diversity libraries for display campaigns — including collaboration with internal computational design and program teams, construction and QC of libraries, and scoping/outsourcing of library construction.
  • Scope and deploy methods and tools to screen against challenging targets.
  • Deploy and scale automation (Biomek, Lynx, Hamilton, or similar) to accelerate display campaigns and library QC.
  • Partner with Data Science / AI/ML on library design and hit‑to‑lead optimization — executing display screens on ML‑designed variants and leveraging computational models to inform display strategies.
  • Partner closely with the Antibody Protein Engineering team on hit‑to‑lead transitions and lead‑molecule optimization campaigns.
  • Evaluate and integrate emerging display platforms and directed evolution methods to expand Prellis’s engineering capabilities.
  • Drive and/or facilitate downstream characterization of optimized antibodies, including detailed biophysical and functional characterization.

Qualifications

  • Scientist (PhD + 2+ years relevant industry experience) or Principal Associate Scientist (BSc/MSc + 5+ years direct relevant industry experience) in molecular/cell biology, biochemistry, immunology, or related discipline.
  • Hands‑on experience developing and deploying display platforms (yeast, phage, mammalian, ribosome, or comparable) for antibody discovery and improvement.
  • Hands‑on experience scoping, designing, and constructing diversity libraries for antibody engineering and improvement; working understanding of current state‑of‑the‑art methods and products regarding diversity libraries.
  • Familiarity with directed evolution methods (site-directed, error‑prone, CDR‑walking, continuous evolution / PACE / OrthoRep, or comparable).
  • Skilled in DNA assembly, cloning, cell‑based assays, flow cytometry / FACS, and mammalian cell culture.
  • Working understanding of biophysical antibody characterization assays (e.g. SPR, BLI, ELISA) and experience effectively using them to achieve engineering goals.
  • Experience with vendor / CRO oversight for library synthesis and antibody characterization.
  • Strong quantitative data analysis skills with working understanding of statistics.
  • Strong project management skills from planning to execution, with track record of successfully driving and completing complex technical projects.
  • Excellent communication skills for both technical and non‑technical audiences; able to work independently and collaboratively across teams.

Bonus Experience

  • Hands‑on experience with continuous directed evolution (OrthoRep, PACE, or comparable).
  • Hands‑on experience in multiple display platforms (e.g. mammalian, ribosome, phage) and ability to make recommendations based on goal.
  • Hands‑on experience designing and driving pooled screening campaigns for antibody/protein engineering, including lentivirus generation, transduction, selection/enrichment strategy, and genotyping.
  • Working understanding of laboratory automation platforms (e.g. Biomek, Lynx, Hamilton, Tecan) and how to effectively deploy them to accelerate antibody/protein engineering projects.

About Prellis Biologics

At Prellis we integrate human biology with machine learning. We aim to revolutionize drug discovery by harnessing the power of the human immune system with tightly integrated machine learning to develop next‑generation antibody therapeutics with unparalleled speed, precision, and safety. We are committed to empowering our pharmaceutical partners with access to the most promising fully human antibody candidates rapidly identified from the human immune repertoire, enabling them to bring life‑changing treatments to patients faster than ever before. Prellis Biologics is a pre‑IPO biotech located in Berkeley, CA with a team‑oriented, inclusive, and family‑friendly culture. Our growing pipeline targets high unmet patient needs across therapeutic areas including metabolic disease, inflammation, and oncology. Prellis has raised funding from top investors, including Celesta, Khosla Ventures, SOSV, and Avidity Partners.

What You Can Expect Of Us

As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well‑being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.

Actual salary will vary based on several factors including, but not limited to, relevant skills, experience, and qualifications.

In addition to the base salary, Prellis offers compelling benefits based on eligibility, comprising health and welfare plans for staff and eligible dependents, financial plans with opportunities to save toward retirement or other goals, work/life balance, and career development opportunities that may include:

  • A competitive employee benefits package, including group medical, dental, and vision coverage, life and disability insurance, flexible spending accounts, and a 401(k) plan
  • Stock-based long‑term incentives
  • Holiday package including a 1+ week winter shutdown

Application Deadline

Prellis does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.

Prellis Bio is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status. We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams — grounded in a wide range of expertise and life experiences — and work even harder to ensure those teams thrive in inclusive, growth‑oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process.

$111,000 - $169,000 per year

Join Our Team
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