Platform Engineer (DevX)

K2 Space•Los Angeles, CA

27d•$123,000 - $160,000

About The Position

The Enterprise Information Systems (EIS) team builds the digital backbone behind K2's satellite design, manufacturing, and mission operations, connecting engineering, production, and mission control so our hardware moves from concept to orbit faster. We're looking for a Platform Engineer who believes developer experience is a first-class engineering discipline. You'll build the deployment patterns, guardrails, and tooling that let engineers from many backgrounds, including those building with AI assistance, ship secure, reliable applications with minimal friction.

Requirements

Bachelor's degree in Computer Science or a related field, or equivalent professional experience, plus 2+ years building developer tooling, platforms, or deployment patterns.
Proficiency in at least one of Go, TypeScript, Python, or C#.
Hands-on experience deploying applications on Amazon EKS.
Experience with CI/CD (GitHub Actions or GitLab CI) and disciplined Git workflows (branching, reviews, automated gates).
Practical experience with AI-assisted development and a clear view of its benefits and pitfalls.

Nice To Haves

Infrastructure as code with Terraform or AWS CDK.
Progressive delivery, such as canary rollouts, and policy-as-code.
Observability experience with metrics, tracing, structured logs, L4/L7 telemetry, and SLI/SLO definition.
Experience building internal developer platforms or golden-path tooling.
Experience supporting manufacturing, aerospace, or other hardware-driven organizations.

Responsibilities

Build and maintain golden-path deployment patterns for Amazon EKS, with built-in guardrails that get applications to production faster.
Help drive secure, consistent AI adoption across software development, including guardrails, governance, observability, and operational support for AI-assisted workflows.
Build automated policies and CI/CD gates that enforce code quality, test coverage, and safe deployments.
Integrate end-to-end monitoring, alerting, and SLOs into the platform.
Track performance and cost (latency budgets, cross-AZ transfer, IP consumption, load balancer efficiency) and act on anomalies.
Write approachable docs and diagnostics, such as guides, diagrams, and "why can't this connect?" flows, that shorten support loops.
Run proofs of concept on new AI platforms, models, and developer experience tools.
Learn the manufacturing domain and make complex concerns like service identity and secure communication simple for platform users.