Build and optimize engineering infrastructure to bridge fast-paced ML research and production. Refactor and harden experimental code, implement/train/evaluate PyTorch models, rigorously test and profile systems for correctness and performance, and balance rapid iteration with pragmatic architectural decisions to enable production-ready research.

Lead research and design of large-scale Biological Foundation Models (BioFMs) using genomic and single-cell data. Develop novel architectures and pretraining paradigms, implement and train models at scale, collaborate with biologists and drug developers, mentor junior researchers, and publish findings in top-tier venues.

Own and evolve ML infrastructure: maintain GCP via Terraform, manage IAM/RBAC, run CI/CD (CircleCI, GitHub Actions), administer workflow orchestration (Nextflow/Seqera, Argo, Kubeflow), manage experiment tracking (W&B, MLflow), build containerized environments and Kubernetes clusters, provision and debug GPU resources, write Python tooling, and deploy and monitor ML models in production.