Senior Scientist, ML Protein Engineering

Job Description

Our company’s Discovery Biologics Disruptive Technologies is seeking an exceptional AI/ML protein engineer to drive the advancement and application of structure-based computational protein design and optimization methods to support our biotherapeutic portfolio. In addition to planning and executing experiments to improve and evaluate technical capabilities, the individual is expected to partner closely with cross‑functional discovery programs to turn great ideas into valuable medicines that help patients.

The role is based out of our Cambridge, MA facility.

We are on a quest for cures and are committed to be the world’s premier, most research‑intensive biopharmaceutical company. Our teams combine leading drug discovery capabilities and world‑class R&D with the purpose of turning breakthrough science into life‑changing medicines.

We recognize that the diversity in our team is our strength and are committed to creating an inclusive environment for all employees. Successful candidates must demonstrate inclusive behaviors in working with a diverse group of scientists to drive our core mission.

In this role, a successful candidate will partner with computational and wet lab researchers to design experiments, identify and develop technologies, and create impactful medicines.

• Create novel biologic therapeutic proteins using structure‑based generative AI and machine learning‑based protein design and engineering technologies.
• Use cutting‑edge AI and train ML models to integrate structural data with experimental datasets to advance therapeutic programs (e.g. NGS, affinity, stability, solubility).
• Improve computational strategies, methods and models (zero‑ and few‑shot) by analyzing protein structure and experimental datasets.
• Devise and evaluate structure‑based protein design strategies to create therapeutics.
• Collaborate with laboratory scientists to ensure experiments that produce high‑quality, ML‑ready data.
• Foster a high‑performance culture of collaboration, engagement, self‑accountability and inclusion.
• Effectively partner across multidisciplinary interfaces to achieve organizational objectives.
• Assess external advancements in the relevant scientific fields.

• PhD and minimum of 0‑3 years of industry experience, a M.S. and a minimum of 4 years of industry experience, or a BS and a minimum of 7 years industry experience.
• Extensive expertise with GenAI/ML-based protein design methods.
• Proven industry track record of success working with cross‑functional teams.
• Commitment to scientific excellence and rigor.
• Excellent communication and collaboration skills.
• Champion for diverse and inclusive culture.
• Strong external reputation with high‑impact publications, presentations, and scientific community engagement.
• Passion for sharing knowledge and helping colleagues develop.

• Expert‑level knowledge of structure‑based generative AI methods for protein design and engineering, including de novo design, lead optimization and in silico prediction.
• Experience training/validating data‑driven ML models to solve complex problems.
• Deep understanding of protein structure and sequence representations, featurization and embeddings.
• Mastery of protein structural analysis and design tools (e.g. Alpha Fold, Rosetta, RF Diffusion, protein MPNN, or similar).
• Strong skills in data analysis and visualization.
• Comprehensive scientific knowledge across workflows and domains integral to the discovery of protein therapeutics.

• Strong hands‑on experience creating and validating workflows to analyze NGS data sets for binder screening and affinity ranking.
• Expertise with bioinformatics tools and software for NGS data analysis: QC, statistical analysis, normalization, alignment, and variant calling.
• Demonstrated proficiency in computational protein design method/algorithm development.

Our company is committed to inclusion, ensuring that candidates can engage in a hiring process that exhibits their true capabilities. Please  if you need an accommodation during the application or hiring process.

As an Equal Employment…