Principal AI Engineer (GenAI) - Molecular Discovery

Role 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.

Responsibilities

• 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, Schr√∂dinger LiveDesign, AlphaFold) for CADD, sequence design and developability assessment.
• Track, evaluate, and train latest molecular prediction & design models/tools from literature and open-source community.

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 (Schrodinger, 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

• M.S. or Ph.D. in Computer Science, Machine Learning, Computational Chemistry/Biology, or related field; Cloud-architect certification a plus.