Cloud Platform - ML Infrastructure Engineer

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.
• Proven track record building self-service ML platforms, pipeline abstraction layers, or automated developer workflows.
• Experience with working with embedded engineers.

Why join us

• Demand for online food delivery is growing really fast! In the last 5 years, just in the US, the overall market has expanded 10X from $10B to $100B, and could expand to $500bn- $1T by 2030.
• Changing the restaurant industry: You’ll be part of a team that helps restaurants succeed in online food delivery.
• Collaborative environment: You will receive support and guidance from experienced colleagues and managers, helping you to learn, grow and achieve your goals, and you’ll work closely with other teams to ensure our customer’s success.

What else you need to know

This role is based in our Pittsburgh office. As a company driven by invention and continuous change - we are constantly reimagining our industries, building new products, and refining how we operate. We do our best work together. That’s why all of our office-based teams work onsite, five days a week.

The base salary range for this role is $140,000 - $174,500 per year.

Actual compensation will be determined on an individual basis and may vary depending on experience, skills, and qualifications.

Base salary is just one part of your total rewards package. You may also be eligible for equity awards and an annual performance-based bonus.

Benefits Summary (USA Full-Time Exempt Employees):

• Medical, dental, and vision insurance (multiple plans, incl. HSA options).
• Company-paid life and disability insurance (short- and long-term).
• Voluntary insurance: accident, critical illness, hospital indemnity.
• Optional supplemental life insurance for self, spouse, and children.
• Pet insurance discount.
• 401(k).
• Health Savings Account (HSA)
• Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
• Time Off policies:
• Discretionary vacation days
• 8 paid holidays per year
• Paid sick time
• Paid Bereavement leave
• Paid Parental Leave

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