Software Engineer, Cloud Platform

Baseten
San Francisco +4 more
Hybrid

Who this role is best for

Best suited to infrastructure-focused engineers with Kubernetes expertise working in AI and machine learning domains.

Best fit for

  • Infrastructure-focused engineers with strong Kubernetes and automation experience in AI environments
    — “Join Cloud Platform Engineering to own the Kubernetes fleet that runs our production AI workloads.”
  • Candidates who can unify platform configuration under GitOps and manage multi-cloud deployments
    — “Unify Kubernetes platform configuration under GitOps, replacing duplicated cluster manifests with shared configuration and explicit provider overrides.”
  • Individuals experienced in building maintainable systems with a focus on observability and failure handling
    — “Build observability into automation so engineers can understand progress, diagnose failures, and retry operations safely.”

Things to consider

  • The role demands a high degree of ownership and responsibility across the entire infrastructure lifecycle
    — “You’ll build APIs, controllers, and automation that make infrastructure easier to operate, reduce configuration drift, and bring GPU capacity online seamlessly.”
  • Candidates must be comfortable with staged rollouts and recovery paths in production environments
    — “Make infrastructure changes safe through validation, staged rollouts, clear ownership boundaries, and tested recovery paths.”

How to stand out

  • Highlight experience with Kubernetes controllers and multi-cloud infrastructure management in your resume and interviews
    — “Experience managing Kubernetes fleets across multiple clouds or GPU infrastructure providers.”
  • Emphasize your background in developing reusable infrastructure templates and GitOps tools
    — “Develop reusable Terraform modules and GitOps configuration that work across cloud and GPU infrastructure providers.”
  • Demonstrate your ability to solve ambiguous operational problems with maintainable software
    — “Ability to turn ambiguous operational problems into maintainable software and communicate design tradeoffs clearly.”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • automating cluster lifecycle management
  • reducing configuration drift
  • improving GPU capacity management
Typical background
production infrastructure software experiencedistributed systems knowledge

Skills & requirements

Required

KubernetesGo ProgrammingDistributed SystemsInfrastructure As CodeGitopsTerraformFluxArgo CDObservabilityLinuxNetworkingDebugging

Preferred

Kubernetes Fleet ManagementGPU InfrastructureOpen Source Contributions

Stack & domain

KubernetesGoTerraformFluxArgo CDGitopsLinuxNetworkingDebuggingInfrastructure As CodeProblem-solvingCommunicationTeamworkAdaptabilityCollaborationCloud PlatformAIInfrastructureDistributed Systems

About the role

Original posting from Baseten via Ashby

ABOUT BASETEN

Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.

THE ROLE

Join Cloud Platform Engineering to own the Kubernetes fleet that runs our production AI workloads. You’ll turn manual cluster operations into reliable, repeatable systems—from bringing new cloud providers online to rolling out configuration and upgrades safely across the entire fleet.

This is a hands-on software engineering role with ownership from design through production. You’ll build APIs, controllers, and automation that make infrastructure easier to operate, reduce configuration drift, and bring GPU capacity online seamlessly to power the AI inference business growth.

EXAMPLE INITIATIVES

  • Unify Kubernetes platform configuration under GitOps, replacing duplicated cluster manifests with shared configuration and explicit provider overrides.
  • Automate cluster onboarding and provisioning with reusable infrastructure templates, readiness checks, and recoverable workflows.
  • Build controllers and rollout mechanisms for fleet upgrades and workload-aware maintenance.

RESPONSIBILITIES

  • Design, build, and operate services, APIs, and Kubernetes controllers that automate cluster lifecycle management.
  • Develop reusable Terraform modules and GitOps configuration that work across cloud and GPU infrastructure providers.
  • Make infrastructure changes safe through validation, staged rollouts, clear ownership boundaries, and tested recovery paths.
  • Build observability into automation so engineers can understand progress, diagnose failures, and retry operations safely.
  • Partner with infrastructure and workload teams to define platform interfaces, resolve operational pain points, and take projects from design through adoption.

REQUIREMENTS

  • Experience building and owning production infrastructure software or distributed systems.
  • Strong programming skills in Go or another systems or backend language, with the ability to contribute to a Go codebase.
  • Deep experience with Kubernetes infrastructure, including developing controllers or operators and understanding reconciliation, custom resources, and failure handling.
  • Practical experience with infrastructure as code and GitOps tools such as Terraform and Flux or Argo CD.
  • Strong Linux, networking, and debugging fundamentals, with sound judgment about reliability and the impact of production changes.
  • Ability to turn ambiguous operational problems into maintainable software and communicate design tradeoffs clearly.

NICE TO HAVE

  • Experience managing Kubernetes fleets across multiple clouds or GPU infrastructure providers.
  • Contributions to Kubernetes or infrastructure open source projects, or experience with workflow orchestration and progressive delivery.

BENEFITS

  • Competitive compensation, including meaningful equity
  • (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
  • Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
  • Paid parental leave
  • Fertility and family-building stipend through Carrot
  • (U.S. only) Company-facilitated 401(k)
  • Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.

Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.

At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.

We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Source: Baseten careers (Ashby)

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