Senior Platform Engineer

Joby Aviation•San Carlos, CA

37d

About The Position

Imagine a piloted air taxi that takes off vertically, then quietly carries you and your fellow passengers over the congested city streets below, enabling you to spend more time with the people and places that matter most. At Joby, we've been working to make that dream a reality since 2009 and we're now in the final stages of certifying our aircraft with the FAA. With plans to launch our aircraft in the US and Dubai, we're now scaling manufacturing and preparing for the launch of our commercial service. We're looking for a Senior Platform Engineer to build and evolve the engineering platform supporting the software used in Joby's aircraft manufacturing operations. In this role, you'll own core infrastructure, software delivery pipelines, and developer tooling, creating reliable self-service capabilities that help engineers ship software without spending time on repetitive setup and operational work. You'll work closely with software engineering, QA, and IT to make software delivery faster, safer, and more predictable. You'll also help establish practical ways to use AI across development, testing, and operations. This is a high-ownership role with direct impact on the reliability and pace of the systems that support aircraft design, production and operations.

Requirements

B.S. or M.S. in Computer Science, Engineering, or equivalent practical experience.
5+ years in platform, DevOps, or SRE roles, with ownership of production systems used by multiple teams
Strong hands-on experience operating Kubernetes in running clusters in production.
Strong CI/CD, GitOps, and infrastructure-as-code background, ideally with GitHub Actions, ArgoCD, and Terraform
Proven software engineering experience building and maintaining production services, tools, and components in Python
Experience with a major cloud provider, preferably Azure, plus comfort with on-premises infrastructure
Security-minded approach to platform design and strong communication skills across functions and time zones

Nice To Haves

Experience building or scaling an internal developer platform used by multiple teams
Hands-on experience with AI coding tools and agent workflows, and a view on how to apply them across an engineering organization
Experience with observability platforms such as Datadog, and with Proxmox or comparable on-premises virtualization
Experience safely exposing services to external users (API gateways, edge security, multi-tenancy)
Experience with Kafka or other event-streaming platforms
Exposure to regulated or audited environments (e.g., SOX) or to manufacturing / shop-floor systems

Responsibilities

Design and build the delivery system, from CI/CD pipelines to internal tools and self-service capabilities, that removes repetitive setup and operational work from developers' hands and shortens the path from idea to production
Build the pipeline foundations that enable fast, reliable automated testing: ephemeral environments, test data, parallelized execution, and quality gates, in partnership with QA and development teams
Design and operate a shared Kubernetes platform across cloud and on-premises factory sites, with the standardized deployment patterns that help teams onboard and ship consistently
Evaluate and integrate AI into development, testing, and operational workflows, including the infrastructure and guardrails needed to use AI-powered tools safely at scale
Own infrastructure as code, observability, incident response, and post-incident reviews for systems that production lines depend on
Embed security and compliance controls into the platform and pipelines so they are enforced automatically, keeping teams fast while maintaining security and compliance requirements