Cloud Platform - ML Infrastructure Engineer
About this role
Lab37 Robotics builds robotic solutions designed for direct-to-customer food production, aiming to enhance efficiency and quality in the food industry. The cloud platform team creates the underlying systems consumed by data scientists, kitchen operations, and field engineers, powering a fleet of food-production robots.
As a Cloud Platform - ML Infrastructure Engineer, the chosen candidate will take ownership of the cloud platform infrastructure layer. Responsibilities include building cloud infrastructure, ML, ETL, and CI/CD pipelines that transform raw robot telemetry into analytics, while ensuring the reliability and observability of the entire data stack.
The ideal person brings hands-on experience in cloud infrastructure, MLOps, and data engineering at scale. Someone who thrives in a collaborative environment and enjoys partnering with embedded and cloud engineers will find success in this position.
What you will do
- Design, build, and maintain scalable machine learning infrastructure that abstracts storage, pipeline management, and environment setup tasks.
- Implement robust systems for automated model checkpointing, persistent metadata management, and experiment tracking across distributed training runs.
- Create self-service machine learning workflows and tooling to empower engineers and data scientists to focus on core logic and model architecture.
- Build and maintain automated extract, transform, and load (ETL) and data ingestion pipelines that stream and transform raw robot telemetry into clean datasets.
- Contribute to infrastructure-as-code using 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.
- Participate in on-call rotations for platform reliability while tracking model drift, system throughput, and optimizing cloud compute costs.
What you need
- Bring three or more years of professional experience in cloud infrastructure, MLOps, and data engineering at scale.
- Possess hands-on experience designing systems for automated model checkpointing, model registries, and metadata management tools such as MLflow or Kubeflow.
- Demonstrate strong experience with machine learning and cloud workflow orchestration tools like SageMaker, Kubernetes, Argo Workflows, or Bedrock, alongside cloud storage architectures.
- Show a proven track record of building self-service machine learning platforms, pipeline abstraction layers, or automated developer workflows.
- Have prior experience working alongside embedded engineers.
Benefits
- Medical, dental, and vision insurance including HSA options
- Company-paid life and disability insurance
- 401(k) retirement plan
- Health Savings Account and Flexible Spending Accounts for healthcare, dependent care, and commuting
- Discretionary vacation days, eight paid holidays, and paid sick time
- Paid bereavement leave and paid parental leave
Skills and tech stack
Questions candidates ask
What is the remote work policy for this role?
This role is based in the Pittsburgh office. All office-based teams work onsite, five days a week.
What is the salary range for the Cloud Platform - ML Infrastructure Engineer position?
The base salary range is $140,000 to $174,500 per year. Additional compensation may include equity awards and an annual performance-based bonus.
What kind of team will I be working with?
You will work with a small, high-impact platform team that builds systems consumed by data scientists, kitchen operations teams, and field engineers. You will also partner closely with cloud and embedded engineers.
The application is on the official ATOMS Careers page careers page.
Apply on company site