Role overview
As a Senior ML Infrastructure Engineer, you will work directly in the Automation org with the core ML, Ops, and Analytics teams to help improve and build out the infrastructure around model deployment and monitoring. This role is essential to helping scale out the amount of time saving’s Gridware brings to customers.
What you'll work on
- Design, build, and maintain the infrastructure, tooling, and workflows that enable reliable, scalable deployment of ML models to production.
- Develop monitoring and observability systems to track model performance, data drift, data quality, and overall system health.
- Create and maintain end-to-end testing frameworks and simulation environments to validate models and pipelines prior to deployment.
- Work closely with Data Engineering and Platform Engineering teams to ensure ML systems integrate cleanly with broader Gridware infrastructure and operational standards.
- Improve CI/CD pipelines for ML workloads, ensuring reproducibility, safe rollout, and automated rollback strategies.
What we're looking for
- 5+ years of experience building production ML infrastructure
- Strong software engineering skills and proficiency in Python
- Experience with cloud platforms (AWS) and container orchestration (Kubernetes)
- Familiarity with feature stores, model registries, or centralized metadata systems (i.e. MLFlow)
This describes the ideal candidate; many of us have picked up this expertise along the way. Even if you meet only part of this list, we encourage you to apply!
Benefits
Health, Dental & Vision (Gold and Platinum with some providers plans fully covered)
Paid parental leave
Alternating day off (every other Monday)
“Off the Grid”, a two week per year paid break for all employees.
Commuter allowance
Company-paid training
Tags & focus areas
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