ML Infrastructure Engineer

Who we are 

Lab37 Robotics, is a technology company focused on the development and deployment of robots designed specifically for direct-to-customer food production. Our mission is to revolutionize the food industry by creating innovative robotic solutions that enhance efficiency, quality, and customer satisfaction. We are passionate about pushing the boundaries of technology to deliver cutting-edge products that meet the evolving needs of our clients.

What you'll do

Take on ownership of the cloud platform infra layer that powers Lab37's robotics fleet. This spans building Cloud infra, ML, ETL, CI/CD pipelines that turn raw robot telemetry into actionable analytics, managing data discovery, data lineage and contributing to models training pipelines on our robots, and ensuring the observability and reliability of the entire data stack.

Work with a small, high-impact platform team to build the systems that every product team at Lab37 consumes — from data scientists training models, to kitchen operations teams viewing dashboards, to engineers deploying new ML models to robots in the field.

Responsibilities

• Design, build, and maintain scalable ML infrastructure that abstracts away underlying storage, pipeline management, and repetitive environment setup tasks.
• 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, model architecture, and validation.
• Build and maintain automated ETL and data ingestion pipelines that stream and transform raw robot telemetry into clean datasets for training and analytics.
• Contribute to infrastructure-as-code (Terraform) and CI/CD automation for model deployment, data processing, and cloud services.
• Partner with cloud and embedded engineers to streamline model deployment to fleets of robots in the field and participate in on-call rotations for platform reliability.
• Monitoring & Cost: Track model drift, system throughput, and optimize cloud compute costs.

What we're looking for

• 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 etc).
• Strong experience with ML & Cloud workflow orchestration tools (SageMaker, K8, Argo Workflows, Bedrock) and cloud storage architectures.