Data Engineer
Core
Build pipeline infrastructure and tooling to bridge raw chemical data and machine learning models for materials discovery.
Role type
Senior Data Engineer (Scientific Data Infrastructure)
Builds
Scalable data ingestion pipelines, automated workflows for crystallographic and molecular data, and self-serve data platforms for scientists.
Domain
AI-driven materials science and computational chemistry
Deliverable
production ML models
Required skills
Python, large-scale data processing, workflow orchestration (Airflow/Prefect/Dagster/Flyte), containerization (Docker/Kubernetes), CI/CD, DevOps practices, database management
Preferred skills
MLOps practices, scientific computing data handling, materials science/chemistry domain knowledge
Technologies
Python, Airflow, Prefect, Dagster, Flyte, Docker, Kubernetes
Responsibilities
Design and build robust data pipelines for materials science datasets and computational chemistry outputs; integrate diverse data sources including databases, literature, patents, and lab instruments; implement automated quality checks and monitoring systems for data integrity; collaborate with ML researchers to align data pipelines with model training requirements; support real-time data needs for AI-driven experiments.
Seniority
Senior, hands-on IC
