Cloud & ML Infra Engineer — Onsite Pittsburgh

LAB37; Pittsburgh (Allegheny County); On-site; USD 140,000 - 175,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

Accident insurance

Critical illness insurance

Hospital indemnity

Pet insurance

401(k) plan

Health Savings Account

Flexible Spending Accounts

Vacation days

Holidays

Sick time

Bereavement leave

Parental leave

Job summary

Lab37 is seeking a cloud-focused platform engineer to own the infra powering its robotics data stack. You’ll design and operate scalable ML infrastructure, orchestrate workflows, and enable self-service ML for data scientists and engineers.

You will collaborate with cloud and embedded teams to deploy models to fleets, manage data pipelines, and optimize costs, with on-call responsibilities as part of a small, high-impact platform team in Pittsburgh.

Qualifications

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

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.

Skills

Cloud infrastructure

MLOps

Data engineering

Model checkpointing

Model registries

Metadata management

MLflow

Kubeflow

Workflow orchestration

Argo Workflows

Airflow

Prefect

Self-service ML

Embedded collaboration

Terraform

Tools

Terraform

Job description

Lab37 is seeking a cloud-focused platform engineer to own the infra powering its robotics data stack. You’ll design and operate scalable ML infrastructure, orchestrate workflows, and enable self-service ML for data scientists and engineers.

You will collaborate with cloud and embedded teams to deploy models to fleets, manage data pipelines, and optimize costs, with on-call responsibilities as part of a small, high-impact platform team in Pittsburgh.