Principal Machine Learning Engineer
Core
Architect and deploy autonomous agentic systems and foundation models to enable complex biomolecular design and autonomous scientific workflows in drug discovery.
Role type
Principal Machine Learning Engineer (Agentic Systems & Foundation Models)
Builds
Autonomous agent orchestration frameworks, distributed training/inference systems for foundation models, and MLOps/AgentOps infrastructure.
Domain
AI for Drug Discovery (AI4DD), computational biology, chemistry
Required skills
Python, PyTorch, JAX, distributed systems, agent orchestration, MLOps/AgentOps, CI/CD, system observability, scientific reasoning translation
Preferred skills
LLM serving optimization, molecular data modalities (protein sequences, chemical graphs), open-source ML contributions
Technologies
PyTorch, JAX, LangGraph, AWS, HPC
Responsibilities
Architect autonomous agents for multi-step scientific reasoning; design scalable distributed training and inference systems; establish MLOps/AgentOps lifecycle best practices; define long-term engineering roadmap; translate scientific problems into shippable systems
Seniority
Principal, technical leadership & strategy
