Sr. Machine Learning Engineer

Our client is a venture-backed biotech company applying AI to drug discovery, using a proprietary platform to identify novel drug targets and therapeutics from complex biological data.

Own the path from experimental model code to validated, reproducible, production-grade capabilities — model code, model-specific pipelines, inference services, and validation systems used by internal teams, partners, and customers. This is a productionization role, not a research role: researchers set the science and model design, you own the engineering path to production and its reliability. You'll need enough ML depth to debug and reason about model behavior, but won't be designing architectures or running research experiments.

The shared platform underneath (Kubernetes, CI/CD, infrastructure) belongs to Platform Engineering, who you'll partner with closely

• Own the engineering lifecycle from experimental model code to tested, maintainable, performant production systems
• Partner with researchers to understand model behavior and evaluation criteria; own the engineering path to production
• Build and operate inference services, batch workflows, and APIs used internally and by external partners/customers
• Define versioned interfaces across models, pipelines, and downstream applications
• Set model delivery practices on top of shared platform systems — versioning, testing, release management, monitoring, rollback
• Build validation/regression systems keeping production aligned with research baselines
• Profile and optimize model performance (throughput, latency, cost), escalating platform-level issues to Platform Engineering
• Lead cross-functional production ML initiatives from investigation through deployment and ongoing operation

• M.S. with 5+ years in ML engineering, MLOps, research engineering, or software engineering for ML
• Strong Python and PyTorch (or comparable) skills — debugging, testing, profiling, product ionizing models
• Enough ML depth to read research code and reason about model behavior in a production contest
• Demonstrated experience partnering with researchers/scientists to move experimental work into production
• Hands-on with inference services, CI/CD, experiment tracking, versioning, and production monitoring
• Experience with Docker, Kubernetes, and cloud infrastructure
• GPU-accelerated training/inference optimization;
  PyTorch internals