Senior Principal Scientist, Machine Learning Computational Immunologist

August 2, 2021


At Bristol Myers Squibb, we are inspired by a single vision – transforming patients’ lives through science. In oncology, hematology, immunology and cardiovascular disease – and one of the most diverse and promising pipelines in the industry – each of our passionate colleagues contribute to innovations that drive meaningful change. We bring a human touch to every treatment we pioneer. Join us and make a difference.

Position Summary

We are seeking a collaborative machine learning scientist with expertise in computational immunology to join the Predictive Sciences team in the San Francisco Bay Area. You will be responsible for the research and development of computational approaches and workflows to identify new antibody targets, discover novel antibody-antigen binding partners, and establish optimal antibody binding characteristics. This role provides the opportunity to leverage large-scale proprietary datasets and work with subject matter experts on cross-functional teams to solve challenging problems in structural bioinformatics and how we engineer the next generation of biotherapeutics.


Pioneer machine learning research into antibody-antigen interaction modeling and design using internal and external datasets.

Partner with project teams to advance sequence- and structure-based antibody discovery and design.

Develop and implement computational approaches and prediction algorithms that can map linear and three-dimensional epitopes, optimize binding scaffolds and library design to guide screening, identify and mature the affinity of antibody hits, and assess potential liabilities and optimal development options.

Communicate findings and recommend follow-up actions in multiple settings such as 1:1, seminars, group, and project meetings.

Experience and Qualifications

Ph.D. in structural bioinformatics, computer science, engineering, statistics, or similar with 8+ years of relevant experience.

Proficient with cutting-edge machine learning approaches.

In-depth understanding of deep learning, Monte-Carlo tree search, genetic, multi-task, and reinforcement learning algorithms.

Proven track record of contributing to and helping advance multi-disciplinary team projects.

Expertise with a high-level programming language such as R or Python for sophisticated data analysis and reproducible research practices.

Scientific curiosity with an ability to identify questions machine learning approaches can address, and the skills to develop solutions both independently and collaboratively.

Excellent problem-solving skills and teamwork.

Strong communication, data presentation, and visualization skills.

Knowledge in biochemistry or protein engineering is a plus.

Around the world, we are passionate about making an impact on the lives of patients with serious diseases. Empowered to apply our individual talents and diverse perspectives in an inclusive culture, our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives.

Our company is committed to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace adjustments and ongoing support in their roles. Applicants can request an approval of accommodation prior to accepting a job offer. If you require reasonable accommodation in completing this application, or any part of the recruitment process direct your inquiries to Visit to access our complete Equal Employment Opportunity statement.


Contact Aditya Radhakrishnan ( with questions.

Application details