42dot Cloud Infrastructure Engineer at 42dot builds and operates cloud infrastructure for autonomous driving data and ML workloads. Works with AWS, Kubernetes, and cloud data platforms.
Responsibilities
42dot’s Autonomy organization is seeking a Cloud Infrastructure Engineer to build and operate the cloud infrastructure that powers our autonomous driving data and ML workloads. You will work across AWS, Kubernetes, and cloud data platforms to enable reliable, scalable, and efficient development and production environments. Partnering closely with the Data, and ML Platform teams, you will establish reusable infrastructure patterns and apply DataOps and MLOps best practices to accelerate the development and deployment of next-generation autonomous driving technologies.
Lead, build, test, and release complex infrastructure services for the autonomy organization on AWS.
Drive upgrades and improvements to our Kubernetes infrastructure and build developer-facing tooling that improves 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 drive root-cause analysis of production incidents.
Build automated workflows and developer tooling so the Data, and ML Platform teams can deploy and operate services with less manual work.
Build and optimize highly reliable, scalable, and distributed infrastructure using a microservice architecture.
Collaborate with security and privacy to perform audits and mitigate findings.
Qualification
Proficient in Python or GoExperience with observabilityLinux internalsExperience at an autonomous-drivingUnderstanding of data governanceInterview Process
Required
Bachelor's degree or higher in Computer Science, Engineering, or a similar technical field.
Minimum of 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 (AWS, Amazon EKS).
Hands-on experience with infrastructure as code and configuration management (Terraform, Pulumi, CloudFormation, or CDK).
Experience monitoring production systems and performing root-cause analysis (Datadog, Prometheus, or CloudWatch).
Proficient in Python or Go, with hands-on experience using Git-based tooling for automation.
Experience with CI/CD and GitOps (GitLab, Argo CD, or similar).
Preferred
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.
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.
