Principal, Machine Learning Engineer
Core
Design, build, and scale ML infrastructure for generative models and reasoning frameworks powering automated scientific discovery in life sciences.
Role type
Principal, hands-on IC Machine Learning Engineer
Builds
Large-scale training pipelines, distributed compute systems, and production ML serving infrastructure for biological sequence design and molecular structure prediction.
Domain
Life Sciences (Biotech/Pharma) + Machine Learning Systems Engineering
Deliverable
production ML models
Required skills
Distributed training infrastructure, large-scale GPU cluster management, system design, production-grade code, CI/CD, observability, PyTorch/JAX/TensorFlow, cross-functional collaboration with researchers
Preferred skills
Generative models for biological sequences, agentic frameworks, active learning loops, open-source ML contributions, life science domain familiarity
Technologies
PyTorch, JAX, TensorFlow, AWS, GCP
Responsibilities
Design and optimize large-scale training pipelines for generative models; own production ML systems end-to-end including deployment and monitoring; architect ML infrastructure for rapid iteration; drive engineering standards and tooling; translate research prototypes into robust production systems.
Seniority
Principal, hands-on IC
