Bristol-Myers Squibb
Principal AI Engineer (GenAI) - Molecular Discovery
Bristol-Myers Squibb, San Diego, California, United States, 92189
Overview
Working with Us Challenging. Meaningful. Life-changing. Bristol Myers Squibb offers work that transforms the lives of patients and the careers of those who do it. You’ll grow and thrive through opportunities across scale and scope, alongside high-achieving teams. Bristol Myers Squibb supports balance and flexibility in our work environment. We offer competitive benefits and programs to help employees pursue their goals at work and in their personal lives. Further information is available on the company careers site. Summary
Own the strategy and delivery of GenAI-native applications, predictive-model workflows, and insight-driven analytics platforms that accelerate molecule discovery. 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. Responsibilities
Molecular Discovery ML Enablement
– Champion predictive-model use-cases across small and large molecule discovery (e.g., property prediction, sequence optimization, generative design).
Design and build platforms that orchestrate structure- and sequence-prediction toolkits (RDKit, OpenEye, Schrödinger LiveDesign, AlphaFold) for CADD, sequence design, and developability assessment.
Track, evaluate, and train latest molecular prediction and design models/tools from literature and the open-source community.
AI-Driven Scientific Applications
– Build scientifically grounded conversational analytics, automated reports, and copilot workflows that guide scientists through complex SAR, sequence datasets, and tools.
Deliver full-stack applications with React/Next.js frontends and Python/FastAPI & GraphQL services that surface models and analytics at scale.
Model-Ops & Engineering Excellence
– Stand up automated pipelines for data curation, experiment tracking, CI/CD, and governed model release (PyTorch/TensorFlow with MLflow/Kubeflow/SageMaker and GitHub Actions).
Package and deploy predictive applications and model endpoints to cloud-native MLOps or on-prem containers for scalable inference and performant access.
Codify reusable templates, inner-source libraries, and design systems to cut feature time-to-value by 40%.
Leadership & Collaboration
– Mentor cross-disciplinary teams of full-stack and ML engineers; promote code quality, documentation, and UX research; partner with discovery leads, IT operations, and external vendors to align backlogs with milestones and data-quality standards; influence budgeting and make-vs-buy decisions for AI tooling and platform enhancements.
Qualifications
Deep Discovery & Molecular Tooling Context
– 7+ years with a relevant advanced degree building/supporting platforms and tools for computational compound design and protein engineering workflows; fluent in SAR analysis, sequence/structure predictions, and assay lifecycles (e.g., Schrödinger, OpenEye, MOE, AlphaFold).
GenAI Engineering Depth
– Demonstrated success building GenAI applications and agentic workflows; fine-tuning and deploying LLMs, diffusion models, structure-prediction models (AlphaFold family, RoseTTAFold), 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 both executives and bench scientists.
Preferred 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/applying Kubernetes/EKS, serverless, or on-prem containers; knowledge of GPU runtime tuning or Triton-based multi-model serving; experience crafting templates or inner-source libraries; cloud certifications (AWS Pro, Azure Expert); multi-cloud deployment (AWS, Azure, GCP).
Education / Credentials
– M.S. or Ph.D. in Computer Science, ML, Computational Chemistry/Biology, or related field; cloud-architect certification a plus. Join Us: Empower researchers with AI tools, agentic workflows, and insight-driven applications to invent the next generation of therapeutics—faster, smarter, and at scale. The starting compensation for this job is a range from $155,000 - $170,000, plus incentive cash and stock opportunities (based on eligibility). The starting pay rate takes into account characteristics of the job, such as required skills and where the job is performed. Final, individual compensation will be decided based on demonstrated experience. Eligibility for benefits varies by job and location. For more on benefits, please visit the company careers site. Benefit offerings may include medical, dental, vision, wellbeing programs, 401(k), disability and life insurance, travel protection, and other programs. Details are subject to plans and applicable laws. On-site protocol: roles may be site-essential, hybrid, field-based, or remote-by-design. Reasonable accommodations are available in the recruitment process. BMS supports inclusion and equal opportunity in employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or protected veteran status. For California residents, additional information is available on the California residents page. Data privacy: any data processed in connection with role applications will be handled in accordance with applicable laws.
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Working with Us Challenging. Meaningful. Life-changing. Bristol Myers Squibb offers work that transforms the lives of patients and the careers of those who do it. You’ll grow and thrive through opportunities across scale and scope, alongside high-achieving teams. Bristol Myers Squibb supports balance and flexibility in our work environment. We offer competitive benefits and programs to help employees pursue their goals at work and in their personal lives. Further information is available on the company careers site. Summary
Own the strategy and delivery of GenAI-native applications, predictive-model workflows, and insight-driven analytics platforms that accelerate molecule discovery. 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. Responsibilities
Molecular Discovery ML Enablement
– Champion predictive-model use-cases across small and large molecule discovery (e.g., property prediction, sequence optimization, generative design).
Design and build platforms that orchestrate structure- and sequence-prediction toolkits (RDKit, OpenEye, Schrödinger LiveDesign, AlphaFold) for CADD, sequence design, and developability assessment.
Track, evaluate, and train latest molecular prediction and design models/tools from literature and the open-source community.
AI-Driven Scientific Applications
– Build scientifically grounded conversational analytics, automated reports, and copilot workflows that guide scientists through complex SAR, sequence datasets, and tools.
Deliver full-stack applications with React/Next.js frontends and Python/FastAPI & GraphQL services that surface models and analytics at scale.
Model-Ops & Engineering Excellence
– Stand up automated pipelines for data curation, experiment tracking, CI/CD, and governed model release (PyTorch/TensorFlow with MLflow/Kubeflow/SageMaker and GitHub Actions).
Package and deploy predictive applications and model endpoints to cloud-native MLOps or on-prem containers for scalable inference and performant access.
Codify reusable templates, inner-source libraries, and design systems to cut feature time-to-value by 40%.
Leadership & Collaboration
– Mentor cross-disciplinary teams of full-stack and ML engineers; promote code quality, documentation, and UX research; partner with discovery leads, IT operations, and external vendors to align backlogs with milestones and data-quality standards; influence budgeting and make-vs-buy decisions for AI tooling and platform enhancements.
Qualifications
Deep Discovery & Molecular Tooling Context
– 7+ years with a relevant advanced degree building/supporting platforms and tools for computational compound design and protein engineering workflows; fluent in SAR analysis, sequence/structure predictions, and assay lifecycles (e.g., Schrödinger, OpenEye, MOE, AlphaFold).
GenAI Engineering Depth
– Demonstrated success building GenAI applications and agentic workflows; fine-tuning and deploying LLMs, diffusion models, structure-prediction models (AlphaFold family, RoseTTAFold), 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 both executives and bench scientists.
Preferred 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/applying Kubernetes/EKS, serverless, or on-prem containers; knowledge of GPU runtime tuning or Triton-based multi-model serving; experience crafting templates or inner-source libraries; cloud certifications (AWS Pro, Azure Expert); multi-cloud deployment (AWS, Azure, GCP).
Education / Credentials
– M.S. or Ph.D. in Computer Science, ML, Computational Chemistry/Biology, or related field; cloud-architect certification a plus. Join Us: Empower researchers with AI tools, agentic workflows, and insight-driven applications to invent the next generation of therapeutics—faster, smarter, and at scale. The starting compensation for this job is a range from $155,000 - $170,000, plus incentive cash and stock opportunities (based on eligibility). The starting pay rate takes into account characteristics of the job, such as required skills and where the job is performed. Final, individual compensation will be decided based on demonstrated experience. Eligibility for benefits varies by job and location. For more on benefits, please visit the company careers site. Benefit offerings may include medical, dental, vision, wellbeing programs, 401(k), disability and life insurance, travel protection, and other programs. Details are subject to plans and applicable laws. On-site protocol: roles may be site-essential, hybrid, field-based, or remote-by-design. Reasonable accommodations are available in the recruitment process. BMS supports inclusion and equal opportunity in employment. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or protected veteran status. For California residents, additional information is available on the California residents page. Data privacy: any data processed in connection with role applications will be handled in accordance with applicable laws.
#J-18808-Ljbffr