Cloud Infrastructure Engineer

42dot • San Francisco, CA

2d • $145,000 - $240,000

About The Position

42dot’s Autonomy organization is seeking a Cloud Infrastructure Engineer to build and operate the cloud infrastructure supporting autonomous driving data and machine learning workloads. The role works across AWS, Kubernetes, and cloud data platforms to enable reliable, scalable, and efficient development and production environments. In close partnership with the Data and ML Platform teams, the engineer will establish reusable infrastructure patterns and apply DataOps and MLOps practices to accelerate development and deployment of autonomous driving technologies.

Requirements

• Bachelor's degree or higher in Computer Science, Engineering, or a similar technical field.
• At least 5 years of experience in software engineering, cloud infrastructure, or ML/Dev/Data Ops.
• Production experience with Kubernetes and core cloud services across compute, storage, networking, identity, and secrets, including AWS and Amazon EKS.
• Hands-on experience with infrastructure as code and configuration management, such as Terraform, Pulumi, CloudFormation, or CDK.
• Experience monitoring production systems and performing root-cause analysis using tools such as Datadog, Prometheus, or CloudWatch.
• Proficiency in Python or Go, with hands-on experience using Git-based tooling for automation.
• Experience with CI/CD and GitOps, such as GitLab or Argo CD.

Nice To Haves

• Deep understanding of Kubernetes internals and ecosystem tooling such as Helm.
• Experience with observability, including metrics and distributed tracing.
• Experience with GPU or ML platform workloads.
• Knowledge of Linux internals, networking, and distributed systems.
• Experience at an autonomous-driving or robotics company.
• Understanding of data governance, data privacy, and security controls for cloud infrastructure.

Responsibilities

• Lead, build, test, and release complex AWS infrastructure services for the autonomy organization.
• Drive Kubernetes infrastructure upgrades and improvements, and build developer-facing tooling to improve development velocity.
• Set up fault-tolerant, multi-region environments for data operations and data applications.
• Own CI/CD pipelines for application and data projects.
• Define the on-call strategy, participate in on-call rotations, and perform root-cause analysis of production incidents.
• Build automated workflows and developer tooling to reduce manual work for the Data and ML Platform teams.
• Build and optimize highly reliable, scalable, distributed infrastructure using a microservice architecture.
• Collaborate with security and privacy teams to perform audits and mitigate findings.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level