Principal AI Engineer; GenAI - Molecular Discovery

Position: Principal AI Engineer (GenAI) - Molecular Discovery
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 usual. 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 our employees with the resources to pursue their goals, both at work and in their personal lives. Read more:

Summary:

Own the strategy and delivery of LLM-native applications, agentic 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.

Agentic LLM-Driven Scientific Applications

* Using Agentic LLM frameworks, build scientifically grounded conversational analytics, automated reports, and "copilot" workflows with complex scientific datasets and tools.

* Deliver full-stack applications, React/Next.js fronts with Python/FastAPI & Graph

QL services - that surface models and analytics at scale.

Molecular Discovery ML Enablement

* Champion predictive-model use-cases across small and large molecule discovery (e.g., property prediction, sequence optimization, generative design).

* Familiarity platforms that orchestrate cutting-edge structure- and sequence-prediction toolkits (RDKit, Open Eye, Schrödinger Live Design, Alpha Fold) for CADD, sequence design and develop ability assessment.

Model-Ops & Engineering Excellence

* Stand up automated pipelines for data curation, experiment tracking, CI/CD, and governed model release (PyTorch/Tensor Flow + MLflow/Kubeflow/Sage Maker + Git Hub 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, data and AI 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

* Agentic LLM engineering depth - Demonstrated success building Agentic LLM workflows and LLM-native Applications; fine-tuning and deploying LLMs, diffusion models, or vision transformers for scientific or operational use-cases.

* Discovery & Molecular Tooling Context - 5+ years with relevant advanced degree building/supporting platforms and tools, for computational compound design and protein engineering workflows (Schrodinger, Open Eye, Alphafold); fluent in SAR analysis, sequence/structure predictions, and assay life cycles.

* Modern MLOps - IaC (Terraform/Cloud Formation), 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…