Software Gigs

Build and operate the data and ML infrastructure powering an AI platform for materials science, owning both sides: data pipelines that ingest and curate large-scale scientific output into training-ready formats, and model packaging, serving, monitoring, and CI/CD systems that move models safely from research to production across customer environments. You will design data ingestion and transformation workflows, implement validation and quality gates, package and version models with reproducible builds, run models through batch and online inference with safe rollout and rollback, monitor for drift and degradation, and build observability and internal tooling for engineering and science teams. The role requires 6+ years shipping production software with deep expertise in data systems, ML infrastructure, containers, orchestration, and observability.

Design, build, and scale ETL/ELT pipelines and cloud data warehouse infrastructure using Python, SQL, and modern orchestration tools like Apache Airflow or Prefect. Own the end-to-end data infrastructure that powers business intelligence and operational reporting, including data quality checks, anomaly detection, and performance optimization. Collaborate with analytics and software engineering teams to define data modeling standards and maintain infrastructure-as-code deployments using Terraform and CI/CD. Requires 3–6 years of data engineering or backend development experience with advanced SQL, Python, and hands-on work with cloud data warehouses like Snowflake or BigQuery.