Machine Learning and Experimental Scientist I/II - Antibody Discovery LAUNCHPAD

Job Ref:JR-6243

Location:450 Brookline Ave, BOSTON, MA 02215

Category:Research Laboratory

Employment Type:Full time

Work Location:Onsite: occasional remote

Salary/Pay Rate:$80,000.00 - $95,900.00 per year

Overview

Dana-Farber Cancer Institute is seeking an experienced PhD-level scientist to join the antibody discovery and antibody-based therapeutics development-focused Lab for Antibody and Nanobody Phage Display and Discovery (LAUNCHPAD). A pre-clinical strategic center, LAUNCHPAD's mission is to streamline "discovery to translation" of antibody-based immunotherapies for cancer. The role also offers the possibility to work closely with faculty in DFCI's Data Science department for ongoing 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 engineering hits to generate potential therapeutics, e.g. bispecific and CAR T-cell immunotherapies. The candidate should be an exceptionally motivated individual with a passion for working in multidisciplinary teams to model, design, screen and engineer immunotherapy candidates and advance next-generation therapies.

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life changing breakthroughs in cancer research and patient care. We are united in our mission of conquering cancer, HIV/AIDS, and related diseases. We strive to create an inclusive, diverse, and equitable environment where we provide compassionate and comprehensive care to patients of all backgrounds, and design programs to promote public health particularly among high-risk and underserved populations. We conduct groundbreaking research that advances treatment, we educate tomorrow's physician/researchers, and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

Responsibilities:

• Provide scientific and technical expertise within multidisciplinary project teams focused on the development of antibody-based immunotherapies.
• Establish, run, and continuously improve machine learning capabilities for antibody discovery, optimization and development.
• Coordinate and maintain the GPU/CPU computational infrastructure provided by DFCI Data Science dept. required to run these tools.
• Develop antibody selection strategies to identify novel, fully human binders from a custom library using yeast-display and drive the optimization & integration of these applications into workstreams.
• Bridge computational and experimental workstreams, support programs with computational and wet lab needs.
• Collaborate with team members across groups and mentor junior lab members.
• Co-author technical reports and manuscripts for publication or presentation at internal and external meetings.

Qualifications:

• PhD scientist with hybrid dry/wet lab hands-on experience in machine learning and antibody discovery and development. A candidate with a M.S. degree and substantial relevant experience (>7 years) may also be considered for this role. Industry research experience is a plus.
• Generative and structure-based protein/antibody design (required): Strong track record, hands-on experience, and in-depth knowledge running antibody discovery tools (e.g., RFdiffusion/RFantibody, BindCraft, BoltzGen, Chai-2, or comparable), inverse-folding methods (ProteinMPNN), and structure prediction (AlphaFold3, RoseTTAFold, ESMFold), applied to affinity maturation, epitope-focused design, and developability triage. Comfort configuring and running these tools in a GPU/CPU compute environment (local, cluster, or cloud) is essential; formal software-engineering experience is not required.
• Antibody discovery and NGS analysis via yeast display (required): generating yeast-display libraries and performing selections for de novo discovery and affinity maturation, and HT analysis of antibody sequence data sets from NGS to assess round-to-round enrichment, in silico developability & hit selection via web-based platforms like PipeBio, Enpicom IGX, Platforma.bio, etc.
• Structural modeling of protein-protein interactions (e.g., MOE, HADDOCK) for epitope/paratope analysis a plus.
• Exceptionally self-motivated and capable of taking scientific initiatives
• Excellent communication (written and verbal) and troubleshooting skills as well as the ability to work with a wide variety of collaborators.
• Outstanding planning, organizational, and multi-tasking skills.
• Must consider attention to detail a strength.
• Ability to thrive in a fast-paced team environment.

Only applicants that submit a cover letter and a detailed CV will be considered. Following a pre-screen interview, the candidate will be requested to provide the contact information for three references.

At Dana-Farber Cancer Institute, we work every day to create an innovative, caring, and inclusive environment where every patient, family, and staff member feels they belong. As relentless as we are in our mission to reduce the burden of cancer for all, we are committed to having faculty and staff who offer multifaceted experiences. Cancer knows no boundaries and when it comes to hiring the most dedicated and compassionate professionals, neither do we. If working in this kind of organization inspires you, we encourage you to apply.

Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.

EEO Poster

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Pay Transparency Statement

The hiring range is based on market pay structures, with individual salaries determined by factors such as business needs, market conditions, internal equity, and based on the candidate's relevant experience, skills and qualifications.

For union positions, the pay range is determined by the Collective Bargaining Agreement (CBA).

$80,000.00 - $95,900.00

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