Staff Forward Deployed Engineer - Kubernetes

Strong software engineering fundamentals: writes well-structured production code with sound design and testing judgment.

Extensive experience serving as the senior or lead engineer in enterprise customer engagements, with a proven ability to lead hands-on customer delivery in the field.

Proven technical leadership through technical direction, mentorship, and stewardship of high-quality engineering practices.

Deep Kubernetes expertise across cluster architecture, networking, storage, RBAC, and permission models, with operator and CRD literacy, Go controllers, Helm authoring, and GitOps via Flux or ArgoCD.

Strong observability expertise across metrics, logs, and traces, with the ability to instrument, troubleshoot, and debug production systems using Prometheus, OpenTelemetry, Datadog, or Grafana.

Hands-on experience building or running MCP servers or AI agent tooling, ideally in production environments.

The anticipated base salary range for this position is$216,000 - $264,000. In addition to base salary, this role offers equity and a comprehensive benefits package.

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Why Join Us?

At Stacklok, we believe great technology is built by teams that support, challenge, and inspire one another. We are AI maximalists, confident in its potential and committed to ensuring it is used in ways that are safe and sustainable.

You will join a highly motivated, collaborative team with deep experience building some of the world's most impactful technologies, with strong roots in open source, cloud-native technologies, security, and developer tools.

We offer competitive compensation and equity, comprehensive medical, dental, and vision coverage, flexible PTO and paid holidays, paid parental leave, and a flexible, hybrid-or-remote work environment, plus team offsites in unique destinations to collaborate and connect in person.

As a Staff Forward Deployed Engineer for Stacklok, you will serve as a technical lead for the US-East and EMEA region.

This is hands-on work in the field: embedding with enterprise customers, taking them from first evaluation to production deployment, and answering the toughest technical questions that emerge along the way.

Beyond customer engagements, the role helps shape technical direction, establishes deployment patterns the broader organization builds on, and mentors engineers across the team.

Field experience feeds directly into design reviews and roadmap discussions, ensuring customer realities help drive product and engineering decisions.

The technical depth is substantial.

The work spans complex Kubernetes deployments and the operational constraints of large enterprise environments, solving the problems that determine whether AI initiatives successfully reach production.