Sr. Scientist, Generative Protein Design

Merck•Cambridge, MA

3d•$131,400 - $206,800•Onsite

About The Position

Our company Discovery Biologics Disruptive Technologies is seeking an exceptional AI/ML protein engineer to drive the application of structure-based computational protein design and optimization methods to support our biotherapeutic portfolio. 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 difference 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 different group of scientists to drive our core mission.

Requirements

Ph.D. in Biochemistry, Computational Biology, Protein Engineering, Physics, Bioengineering, Computer Science, Molecular Biology, Chemistry, Pharmaceutical Science, or a related STEM field and a minimum of 0-3 years of industry experience, or MS and 4+ years industry experience, or B.S. 7+ years industry experience.
Demonstrated success generating de novo proteins with desired in vitro behavior.
Extensive expertise in GenAI/ML-based protein design methods and protein structural biology.
Experience using experimental data to optimize protein sequences for function.
Proven industry track record of success working with cross-functional teams.
Commitment to scientific excellence and rigor.
Excellent communication and collaboration skills.
Champion for different 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 (e.g., RFdiffusion, Boltzgen, and/or BindCraft in combination with ProteinMPNN and/or AlphaFold).
Strong hands-on experience applying experimental data (e.g., affinity, stability, solubility) to optimize protein sequence/structure/function.
Strong skills in data analysis and visualization.
Comprehensive scientific knowledge across workflows and domains integral to the discovery of protein therapeutics.

Nice To Haves

Deep understanding of protein structure and sequence representations, featurization and embeddings.
Demonstrated proficiency in computational protein design method/algorithm development.
Experience training/validating data-driven ML models to solve complex problems.

Responsibilities

Create de novo therapeutic proteins using structure-guided generative AI and machine learning-based protein design and engineering technologies.
Integrate AI/ML protein design methods with structural biology and high-throughput experimental screening data to devise and execute de novo protein design and optimization strategies.
Process and analyze experimental functional data in context of protein structure, and optimize workflows using data for hit-to-lead optimization.
Evaluate and improve computational protein design strategies, methods and models (zero- and few-shot).
Train ML models to integrate structural data with experimental datasets to advance therapeutic programs.
Collaborate with laboratory scientists to design 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.