ML Infra Engineer: Scale GPU Training & Data Pipelines

Humble Robotics; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Humble Robotics in the United States is seeking an ML infrastructure engineer to design, build, and scale the foundational systems powering our autonomous hauler. You will shape data collection, training pipelines, and model deployment, partnering with the ML team to translate needs into reliable, scalable infrastructure.

You will own high-availability cloud services, infrastructure-as-code, CI/CD pipelines, and security across the full data-to-model workflow, from vehicle data ingestion to

Qualifications

• Experience building and operating high-availability web services on cloud infrastructure.
• Experience with infrastructure-as-code and configuration management tools (Terraform and Ansible).
• Experience building and maintaining CI/CD pipelines and managing deployments.
• Fluent in security fundamentals including Linux hardening, network security, and cryptographic principles.
• Hands-on experience with cluster scheduling systems for running large-scale batch computation.
• Comfortable reading, writing, and extending non-trivial code (not just scripting).
• Eligible to work in the United States.

Responsibilities

• Design, build, and scale foundational ML infrastructure powering autonomous hauler systems.
• Develop data collection infrastructure and batch compute pipelines for high-quality training sets.
• Design and scale distributed ML training on GPU clusters.
• Own performance, observability, efficiency, and security across the full pipeline.
• Partner with the ML team to translate workflows into reliable infrastructure that accelerates their work.

Skills

Cloud infrastructure

CI/CD

Security fundamentals

Linux

Scheduling systems

Coding

Tools

Terraform

Ansible

Kubernetes

Job description

Humble Robotics in the United States is seeking an ML infrastructure engineer to design, build, and scale the foundational systems powering our autonomous hauler. You will shape data collection, training pipelines, and model deployment, partnering with the ML team to translate needs into reliable, scalable infrastructure.

You will own high-availability cloud services, infrastructure-as-code, CI/CD pipelines, and security across the full data-to-model workflow, from vehicle data ingestion to