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.