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# Senior Platform Engineer

- **Company:** GumGum, Inc.
- **Location:** Santa Monica, CA, United States
- **Experience:** Expert
- **Salary:** $186,000.0 - $215,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Continuous Integration, DevOps, Github, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Kubernetes, Infrastructure Automation Frameworks, Information Technology
- **Published:** July 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=6cc2c00fa851f1dd

## About the Role

* A bachelor's degree in Computer Science, Engineering, or a related field - or equivalent hands-on experience
 * 5+ years in platform engineering, developer experience, or a closely related discipline
 * Direct, hands-on experience building or driving org-wide adoption of a development platform
 * Working knowledge of AWS, Kubernetes, and Python, with enough depth to partner credibly with DevOps and ML Engineering
 * A track record of building internal tools and platforms that teams actually adopted, not just shipped
 * Strong written and verbal communication - able to pitch an idea, explain a metric to a VP, and win over a skeptical senior engineer, all in the same day
 * Strong, opinionated judgment about where AI genuinely speeds up delivery and where it doesn't
 * A product mindset and the ability to drive buy-in across DevOps, MLE, Security, and Engineering leadership without relying on mandate

## Description

* Evaluate the AI SDLC / spec-driven development landscape (e.g., GitHub Spec Kit, OpenSpec, BMAD-METHOD) and make the call on whether GumGum adopts or builds its own platform
 * Architect the platform end-to-end - spec authoring, agent orchestration, and validation gates - owning the roadmap from prototype to company-wide rollout
 * Build the tooling and services that make spec-driven development the default, paved-road workflow for every engineering team
 * Instrument the platform to track adoption, delivery velocity, and friction, using that data to shape what gets built next
 * Design for self-service, so product managers and other non-engineers can author and iterate on specs on their own
 * Partner closely with DevOps to wire the platform into CI/CD, Kubernetes, and deployment infrastructure
 * Team up with Machine Learning Engineering on model selection, context design, and agent guardrails, and with Security Engineering on authentication and auditability
 * Drive org-wide adoption, building the documentation and training that let new teams pick up the workflow without hand-holding, and track the metrics that prove out a 10x delivery gain


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