Bristol Myers Squibb
Principal AI Engineer (GenAI) - Molecular Discovery
Bristol Myers Squibb, California, Missouri, United States, 65018
Summary
Own the strategy and delivery of Gen AI-native applications, predictive-model workflows, and insight-driven analytics platforms that accelerate large and small-molecule 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. 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 cutting-edge structure- and sequence-prediction toolkits (RDKit, OpenEye, Schrödinger LiveDesign, AlphaFold) for CADD, sequence design, and developability assessment. Track, evaluate, and train latest molecular prediction & design models/tools from literature and the open-source community. AI-Driven Scientific Applications
Using agentic GenAI frameworks, build scientifically grounded conversational analytics, automated reports, and copilot workflows that guide scientists through complex SAR, sequence datasets, and tools. Deliver full-stack applications, React/Next.js fronts with 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 + MLflow/Kubeflow/SageMaker + 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 that cut feature time-to-value by 40%. Leadership & Collaboration
Mentor a cross-disciplinary team of full-stack and ML engineers; foster 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. Qualifications
Deep Discovery & Molecular Tooling Context - 7+ years with relevant advanced degree building/supporting platforms and tools for computational compound design and protein engineering workflows (Schrödinger, OpenEye, MOE, MiXCR, AlphaFold); fluent in SAR analysis, sequence/structure predictions, and assay lifecycles. 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 executives and bench scientists alike. 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 and operating applications/models on Kubernetes/EKS, serverless FaaS, or on-prem containers. Knowledge of GPU runtime tuning 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; Cloud-architect certification a plus. Join us to empower researchers with AI tools, agentic workflows, and insight-driven applications that help 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). Final, individual compensation will be decided based on demonstrated experience.
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Own the strategy and delivery of Gen AI-native applications, predictive-model workflows, and insight-driven analytics platforms that accelerate large and small-molecule 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. 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 cutting-edge structure- and sequence-prediction toolkits (RDKit, OpenEye, Schrödinger LiveDesign, AlphaFold) for CADD, sequence design, and developability assessment. Track, evaluate, and train latest molecular prediction & design models/tools from literature and the open-source community. AI-Driven Scientific Applications
Using agentic GenAI frameworks, build scientifically grounded conversational analytics, automated reports, and copilot workflows that guide scientists through complex SAR, sequence datasets, and tools. Deliver full-stack applications, React/Next.js fronts with 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 + MLflow/Kubeflow/SageMaker + 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 that cut feature time-to-value by 40%. Leadership & Collaboration
Mentor a cross-disciplinary team of full-stack and ML engineers; foster 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. Qualifications
Deep Discovery & Molecular Tooling Context - 7+ years with relevant advanced degree building/supporting platforms and tools for computational compound design and protein engineering workflows (Schrödinger, OpenEye, MOE, MiXCR, AlphaFold); fluent in SAR analysis, sequence/structure predictions, and assay lifecycles. 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 executives and bench scientists alike. 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 and operating applications/models on Kubernetes/EKS, serverless FaaS, or on-prem containers. Knowledge of GPU runtime tuning 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; Cloud-architect certification a plus. Join us to empower researchers with AI tools, agentic workflows, and insight-driven applications that help 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). Final, individual compensation will be decided based on demonstrated experience.
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