Software Engineer, Machine Learning Infrastructure
Skills
- Strong Python proficiency for production and tooling
- Experience with cloud platforms and HPC environments
- Familiarity with orchestration frameworks such as Ray, Argo, or Airflow
- Experience designing CI/CD for ML systems and cross-repository orchestration
- Debugging, logging, and monitoring of distributed compute workloads
- Data pipeline design and ETL for scientific/experimental data
Responsibilities
- Design, implement, and maintain CI/CD pipelines and integration tests for ML workflows
- Architect and enforce cross-repo standards and repository designs for scalable development
- Develop and support Ray-based compute environments including debugging, logging, and maintenance
- Build and maintain data ingestion pipelines linking lab instrumentation and experiments to ML teams
- Collaborate with research engineers and scientists to productionize models and tooling
- Own reliability, observability, and reproducibility practices for ML workloads
Education
- Bachelor’s degree in Computer Science, Engineering, or related technical field (or equivalent experience)
- Advanced degree (MS/PhD) in a relevant discipline is a plus, especially for research-adjacent roles
Benefits
- Competitive salary and equity participation
- Health, dental, and vision benefits
- Flexible time off and collaborative team culture
- Opportunities to work at the intersection of AI and scientific research
- Professional development and conference budget
Average salary estimate
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