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Salary: USD 140,000 - 174,500 / annual
Lab37 Robotics is a technology company developing and deploying robots for direct-to-customer food production. This role owns the cloud platform infrastructure layer that powers Lab37's robotics fleet, spanning cloud infrastructure, ML, ETL, CI/CD pipelines, data discovery, data lineage, and model training pipelines. You will ensure observability and reliability of the entire data stack.
You will work with a small, high-impact platform team to build systems consumed by product teams across Lab37—from data scientists training models to kitchen operations teams viewing dashboards to engineers deploying ML models to robots in the field.
Key responsibilities include:
- Design, build, and maintain scalable ML infrastructure that abstracts storage, pipeline management, and environment setup
- Implement robust systems for automated model checkpointing, persistent metadata management, and experiment tracking across distributed training runs
- Create self-service ML workflows and tooling that empower ML engineers and data scientists to focus on core logic and model architecture
- Build and maintain automated ETL and data ingestion pipelines that stream and transform raw robot telemetry into clean datasets
- Contribute to infrastructure-as-code (Terraform) and CI/CD automation for model deployment and cloud services
- Partner with cloud and embedded engineers to streamline model deployment to robot fleets and participate in on-call rotations
- Monitor model drift, system throughput, and optimize cloud compute costs
The role is based in Pittsburgh and requires onsite work five days per week.
Requirements:
- 3+ years of experience in cloud infrastructure, MLOps, and data engineering at scale
- Hands-on experience designing systems for automated model checkpointing, model registries, and metadata management (e.g., MLflow, Kubeflow)
- Strong experience with cloud workflow orchestration tools (Argo Workflows, Airflow, Prefect, or similar) and cloud storage architectures
- Proven track record building self-service ML platforms, pipeline abstraction layers, or automated developer workflows
- Experience working with embedded engineers
