Bristol-Myers Squibb
Principal AI Engineer (GenAI) - Molecular Discovery
Bristol-Myers Squibb, Florida, New York, United States
Working with Us
Challenging. Meaningful. Life‑changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but ordinary. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high‑achieving teams. Take your career farther than you thought possible.
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 employees with the resources to pursue their goals both at work and in their personal lives. Read more: careers.bms.com/working‑with‑us
Summary Own the strategy and delivery of GenAI‑native applications, predictive‑model workflows, and insight‑driven analytics platforms that accelerate both small‑molecule and biotherapeutic invention. Translate scientific objectives into intuitive software products and robust model‑ops practices that help chemists, protein engineers, and data scientists iterate faster, uncover deeper insights, and make better decisions.
Domain‑Centric AI / ML Enablement
Champion predictive‑model use‑cases across medicinal chemistry and biologics (e.g., property prediction, sequence optimization, generative design).
Harness cutting‑edge structure‑ and sequence‑prediction models (AlphaFold/OpenFold, RoseTTAFold, RFdiffusion, Schrodinger, OpenEye) to accelerate target triage, protein engineering and binding‑interface analysis.
Track, evaluate, and train molecular prediction models and integrate genAI methods from the literature and open‑source community.
Ensure model outputs, metrics, and explainability align with discovery KPIs and downstream lab workflows.
Insight‑Driven Agentic Gen‑AI and Applications
Integrate agentic genAI frameworks (e.g., Bedrock, LangChain, LlamaIndex, AzureOpenAI) to create conversational analytics, automated report writers, and “copilot” agents that guide scientists through complex SAR, sequence, or imaging datasets.
Deliver full‑stack applications—React/Next.js fronts with Python/FastAPI & GraphQL services— that surface models and analytics at scale with sub‑second responsiveness.
Model‑Ops & Engineering Excellence
Stand up automated pipelines for data curation, experiment tracking, CI/CD, and governed model release (PyTorch/TensorFlow + MLflow/Kubeflow/SageMaker + GitHub Actions).
Package and deploy predictive applications and model endpoints to cloud PaaS or on‑prem containers for scalable inference and performant access.
Codify reusable templates, inner‑source libraries, and design systems that cut feature time‑to‑value by 40 %.
Leadership & Collaboration
Mentor a cross‑disciplinary team of full‑stack and ML engineers; foster “better‑than‑best” practices in code quality, documentation, and UX research.
Partner with discovery leads, IT operations, and external vendors to align technical backlogs with portfolio milestones and data‑quality standards.
Influence budgeting and make‑vs‑buy decisions for AI tooling and platform enhancements.
Must‑Have Qualifications
Deep Discovery Context
– 8‑10 yrs building software or ML solutions for medicinal chemistry, biologics engineering, or high‑content screening; fluent in SAR data, sequence/structure relationships, and assay lifecycles.
Molecular Tooling Familiarity
– Practical mastery of open‑source and proprietary molecular‑design toolkits (e.g., EvoDiff, RFdiffusion, Molformer, RDKit, AlphaFold, Schrodinger, OpenEye) and the ability to integrate or adapt them within proprietary pipelines.
Hands‑on GenAI / ML depth
– Demonstrated success fine‑tuning and deploying LLMs, diffusion models, GNNs, structure‑prediction models (AlphaFold family, RoseTTAFold, ESMFold), or vision transformers for scientific or operational use‑cases.
Modern MLOps
– IaC (Terraform/CloudFormation), automated testing, secrets management, continuous model evaluation, lineage tracking.
Influence & communication
– Lead architecture reviews, map tech choices to scientific KPIs, mentor cross‑functional teams, and guide roadmap workshops with executives and bench scientists alike.
Desirable Skills
Contributions to open‑source molecular‑design projects.
Advanced Python & React; shipped production apps that integrate APIs, scale model inference, and manage complex research datasets.
Comfortable packaging and operating applications/models on Kubernetes/EKS, serverless FaaS, or on‑prem containers.
Knowledge of GPU runtime tuning, NVLink optimization, or Triton‑based multi‑model serving.
Experience crafting cookie‑cutter templates or inner‑source libraries that accelerate team velocity.
Cloud‑architect certifications (AWS Pro, Azure Expert, etc.).
Multi‑cloud deployment mastery (AWS, Azure, GCP).
Education / Credentials – M.S. or Ph.D. in Computer Science, Machine Learning, Computational Chemistry/Biology, or related field; 10 + yrs industry experience (or 6 + yrs with advanced degree). Cloud‑architect certification a plus.
Compensation Overview Brisbane, CA – $174,750 – $211,758; Cambridge Crossing – $174,750 – $211,758; Princeton, NJ – $158,870 – $192,507; San Diego, CA – $174,750 – $211,758. The starting compensation range(s) for this role are listed above for a full‑time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay rate takes into account characteristics of the job, such as required skills, location, work schedule, job‑related knowledge, and experience. Final, individual compensation will be decided based on demonstrated experience.
Beware of any benefits that may vary based on the job and location. For more on benefits, visit https://careers.bms.com/life‑at‑bms/.
Join Us: Empower researchers with the AI tools, agentic workflows, and insight‑driven applications they need to invent the next generation of therapeutics—faster, smarter, and at scale.
If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.
#J-18808-Ljbffr
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 employees with the resources to pursue their goals both at work and in their personal lives. Read more: careers.bms.com/working‑with‑us
Summary Own the strategy and delivery of GenAI‑native applications, predictive‑model workflows, and insight‑driven analytics platforms that accelerate both small‑molecule and biotherapeutic invention. Translate scientific objectives into intuitive software products and robust model‑ops practices that help chemists, protein engineers, and data scientists iterate faster, uncover deeper insights, and make better decisions.
Domain‑Centric AI / ML Enablement
Champion predictive‑model use‑cases across medicinal chemistry and biologics (e.g., property prediction, sequence optimization, generative design).
Harness cutting‑edge structure‑ and sequence‑prediction models (AlphaFold/OpenFold, RoseTTAFold, RFdiffusion, Schrodinger, OpenEye) to accelerate target triage, protein engineering and binding‑interface analysis.
Track, evaluate, and train molecular prediction models and integrate genAI methods from the literature and open‑source community.
Ensure model outputs, metrics, and explainability align with discovery KPIs and downstream lab workflows.
Insight‑Driven Agentic Gen‑AI and Applications
Integrate agentic genAI frameworks (e.g., Bedrock, LangChain, LlamaIndex, AzureOpenAI) to create conversational analytics, automated report writers, and “copilot” agents that guide scientists through complex SAR, sequence, or imaging datasets.
Deliver full‑stack applications—React/Next.js fronts with Python/FastAPI & GraphQL services— that surface models and analytics at scale with sub‑second responsiveness.
Model‑Ops & Engineering Excellence
Stand up automated pipelines for data curation, experiment tracking, CI/CD, and governed model release (PyTorch/TensorFlow + MLflow/Kubeflow/SageMaker + GitHub Actions).
Package and deploy predictive applications and model endpoints to cloud PaaS or on‑prem containers for scalable inference and performant access.
Codify reusable templates, inner‑source libraries, and design systems that cut feature time‑to‑value by 40 %.
Leadership & Collaboration
Mentor a cross‑disciplinary team of full‑stack and ML engineers; foster “better‑than‑best” practices in code quality, documentation, and UX research.
Partner with discovery leads, IT operations, and external vendors to align technical backlogs with portfolio milestones and data‑quality standards.
Influence budgeting and make‑vs‑buy decisions for AI tooling and platform enhancements.
Must‑Have Qualifications
Deep Discovery Context
– 8‑10 yrs building software or ML solutions for medicinal chemistry, biologics engineering, or high‑content screening; fluent in SAR data, sequence/structure relationships, and assay lifecycles.
Molecular Tooling Familiarity
– Practical mastery of open‑source and proprietary molecular‑design toolkits (e.g., EvoDiff, RFdiffusion, Molformer, RDKit, AlphaFold, Schrodinger, OpenEye) and the ability to integrate or adapt them within proprietary pipelines.
Hands‑on GenAI / ML depth
– Demonstrated success fine‑tuning and deploying LLMs, diffusion models, GNNs, structure‑prediction models (AlphaFold family, RoseTTAFold, ESMFold), or vision transformers for scientific or operational use‑cases.
Modern MLOps
– IaC (Terraform/CloudFormation), automated testing, secrets management, continuous model evaluation, lineage tracking.
Influence & communication
– Lead architecture reviews, map tech choices to scientific KPIs, mentor cross‑functional teams, and guide roadmap workshops with executives and bench scientists alike.
Desirable Skills
Contributions to open‑source molecular‑design projects.
Advanced Python & React; shipped production apps that integrate APIs, scale model inference, and manage complex research datasets.
Comfortable packaging and operating applications/models on Kubernetes/EKS, serverless FaaS, or on‑prem containers.
Knowledge of GPU runtime tuning, NVLink optimization, or Triton‑based multi‑model serving.
Experience crafting cookie‑cutter templates or inner‑source libraries that accelerate team velocity.
Cloud‑architect certifications (AWS Pro, Azure Expert, etc.).
Multi‑cloud deployment mastery (AWS, Azure, GCP).
Education / Credentials – M.S. or Ph.D. in Computer Science, Machine Learning, Computational Chemistry/Biology, or related field; 10 + yrs industry experience (or 6 + yrs with advanced degree). Cloud‑architect certification a plus.
Compensation Overview Brisbane, CA – $174,750 – $211,758; Cambridge Crossing – $174,750 – $211,758; Princeton, NJ – $158,870 – $192,507; San Diego, CA – $174,750 – $211,758. The starting compensation range(s) for this role are listed above for a full‑time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay rate takes into account characteristics of the job, such as required skills, location, work schedule, job‑related knowledge, and experience. Final, individual compensation will be decided based on demonstrated experience.
Beware of any benefits that may vary based on the job and location. For more on benefits, visit https://careers.bms.com/life‑at‑bms/.
Join Us: Empower researchers with the AI tools, agentic workflows, and insight‑driven applications they need to invent the next generation of therapeutics—faster, smarter, and at scale.
If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.
#J-18808-Ljbffr