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Embedding VC

Early-stage venture fund for Generative AI

Developer Platform Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USANo salary listedIn-person

About the Job

Requirements

  • At least 6 years of experience in backend, platform, infrastructure, or developer tools engineering.
  • Strong proficiency in Go, Java, or Kotlin, with scripting experience in Python.
  • Production experience developing CI/CD pipelines using GitHub Actions, Tekton, Jenkins, or custom pipeline orchestrators.
  • Hands-on experience with GitOps tooling such as ArgoCD, Flux, and Kustomize at scale across 10 or more clusters or environments.
  • Experience with Kubernetes operators, custom controllers, custom resource definitions, and Helm chart development.
  • Understanding of container registries and artifact management, including Harbor, ECR, or Artifactory.
  • Experience with build systems such as Bazel or Gradle, including reproducible builds, remote caching, and build graph optimization.
  • Background in dependency analysis tooling or graph databases such as Neo4j or Dgraph.

Responsibilities

  • Build module-level CI/CD pipelines that publish versioned artifacts, including Docker images and Kubernetes manifests, to an artifact registry, enforcing reproducible builds with hermetic environments, pinned toolchains, and deterministic outputs.
  • Implement build provenance automation, including SLSA attestation generation, artifact signing, and dependency chain verification in every pipeline; validate Golden Signals in CI and block releases missing required metrics endpoints.
  • Develop a dependency graph database that ingests static dependencies, including code imports, API contracts, and schema references, as well as runtime dependencies from service mesh traces, and provides a queryable graph for impact analysis.
  • Build change impact analysis tools that compute affected modules, sites, and tests for a pull request and surface results in the pull request review interface; implement selective test orchestration to run tests shown as relevant by the dependency graph.
  • Implement the GitOps deployment engine using ArgoCD or Flux controllers to manage site configuration repositories, automated reconciliation, staging-to-canary-to-production promotion, Golden Signals monitoring, and automatic rollback.
  • Build site provisioning tools that scaffold new site configuration repositories with module version pinning and compliance parameters, and provide a self-service console for configuration-type sites.
  • Develop a version management console for module version status, version drift visualization across sites, batch upgrade workflows, automated changelog generation, and compatibility checking before deployment.
  • Implement a security patch force-push pipeline that automatically creates SECURITY-REQUIRED pull requests across site repositories, with SLA tracking and escalation triggers.
  • Build a four-layer test orchestrator that selects tests by module, country, and scenario tags, executes tests in parallel at scale, detects coverage gaps, and blocks deployments with actionable feedback.

Desired Qualifications

  • Experience with AWS services such as CodePipeline, CodeBuild, Proton, or Service Catalog, or GCP services such as Cloud Build, Artifact Registry, or Cloud Deploy.
  • Background at Stripe, Adyen, or Square on developer platform teams.
  • Experience building internal developer platforms or platform-as-a-service for engineering organizations.
  • Familiarity with supply chain security tooling such as Sigstore, in-toto, and software bill of materials generation.

About the Company

Embedding VC

Early-stage venture fund for Generative AI

Embedding VC is an early-stage venture fund that backs Generative AI startups. It provides seed to early-stage capital and operational guidance, leveraging a team of current and former builders and operators who can help portfolio companies grow. The fund’s approach centers on working closely with founders and using hands-on experience to scale AI projects, including those aiming toward AGI, during a period described as a new computing super cycle driven by AI innovations. Unlike funds that rely mainly on financial leverage, Embedding VC differentiates itself through its deep operator network, practical support, and focus on and commitment to building capable, scalable AI businesses. The overall goal is to support ambitious AI founders to advance the development and deployment of AI technologies and capture value in the expanding AI ecosystem.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Menlo Park, CA
Founded
2023

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