Machine Learning Infrastructure Engineer
Core
Build and scale cloud-native infrastructure for AI-driven drug design, processing massive biological/chemical datasets and running large-scale model training.
Role type
Senior Machine Learning Infrastructure Engineer
Builds
Production-grade ML systems and data architectures for computational drug discovery
Domain
Life sciences / Computational chemistry
Deliverable
production ML models | infrastructure
Required skills
Python, PyTorch, MLOps, workflow orchestration (Argo/Prefect), cloud data warehousing (BigQuery/Snowflake), Kubernetes, distributed training (Ray/Anyscale)
Preferred skills
GCP, AI-assisted development workflows
Technologies
GCP, Kubernetes, BigQuery, Snowflake, Argo Workflows, Prefect, Ray, Anyscale, PyTorch, NumPy, Pandas
Responsibilities
Build and scale infrastructure for deploying ML models in chemistry and structural biology; Productionize research-grade models into robust systems; Design and maintain cloud-native data architectures for large-scale molecular datasets; Develop and orchestrate ML pipelines; Implement and optimize distributed training and inference workflows; Design and maintain LLM-based agentic systems for automated drug design
Seniority
Senior, hands-on IC
