Staff Forward Deployed Engineer - Kubernetes

About this role

Stacklok is led by CEO Craig McLuckie and CTO Joe Beda , two of the creators of Kubernetes. As AI reshapes how software is built and used, we're building the foundation enterprises need to adopt it with confidence.

We're building the control plane for enterprise AI agents, enabling organizations to run, govern, and secure them on the infrastructure they already trust. Through Model Context Protocol (MCP) servers running across Kubernetes and private cloud environments, AI agents can securely connect to internal data and systems while meeting the security, compliance, and operational requirements of highly regulated and security-conscious organizations. We've also extended this foundation to the model layer with an enterprise AI gateway.

The Stacklok Enterprise Platform is built on ToolHive , our open source MCP platform, and is already being adopted by leading technology companies and organizations in regulated industries. We also help maintain the official MCP registry and contribute openly to the community shaping the future of enterprise AI.

Location

This is a remote role based in North America, with a strong preference for candidates located in the U.S. Eastern Time Zone . The role primarily supports customers across the Eastern Time Zone and EMEA.

Regular travel is not expected. Occasional travel may be required for customer visits, company offsites, conferences, or other business needs.

The Opportunity

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. It is an opportunity to shape how AI reaches production across some of the most demanding organizations in the region.

What Success Looks Like: First 6-12 Months

• Onboarded quickly and led live engagements within the first 90 days, becoming the senior technical leader that customers and the field team rely on across the region.

• Took a Design Partner or enterprise customer from first evaluation to a working production deployment, clearing the hardest Kubernetes and platform blockers along the way.

• Stood up the in-region engagement model from scratch: the runbooks, escalation paths, and deployment standards that every engagement now follows.

• Raised the technical bar across the region, lifting the engineers around them and turning field learnings into a reusable, AI-assisted playbook the whole team adopted.

• Surfaced field insight, from region-specific regulatory needs to recurring customer requirements, that shaped a product or roadmap decision.

In This Role, You Will

• Lead forward deployed engagements end to end, from scoping each customer's technical goals and deployment approach through to a working production deployment.

• Own the region's technical calls as the in-region anchor and escalation point for the hardest platform and Kubernetes problems customers raise.

• Design and deliver changes to how the platform deploys to Kubernetes, unblocking enterprise adoption and contributing those changes back upstream.

• Define the reusable reference architectures and deployment patterns that engagements across the region and the wider team build on.

• Set the technical bar, mentoring engineers through design reviews and pairing and serving as the technical gate for what good looks like in hiring.

• Own how forward deployed work runs in the region, adapting it to how local customers operate and shaping where it hands off to applied AI engineering.

• Establish the AI-assisted practices and tooling the team adopts, and channel field insight into the product roadmap.

Desired Skill & Experience

We do not expect every candidate to meet every point below. If this role excites you and you bring most of it, we encourage you to apply.

• 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.

• AI-first mindset: an active user of AI coding assistants who brings agentic workflows into daily work and experiments with AI to automate and accelerate delivery.

• Communication: excellent written and verbal communication, able to explain complex technical ideas clearly to both technical and non-technical audiences.

• Startup mentality and strong ownership: self-directed and hands-on, comfortable building where no playbook exists yet, and driving clarity through action rather than waiting for it.

Compensation: The anticipated base salary

Similar jobs

• Senior Forward Deployed Engineer - Kubernetes — Stacklok · Remote
• Staff Forward Deployed Engineer — Gitlab · Remote
• Staff Forward Deployed Engineer — Hims-and-hers · Remote
• Staff Forward Deployed Engineer — Handshake · San Francisco, CA
• Staff Forward Deployed Engineer — Afresh · San Francisco, CA
• Staff Forward Deploy Engineer (Fullstack - Java/React) — Sentinellabs · Remote
• Staff Forward Deployed Engineer, AI/ML — Digitalocean98 · Seattle
• Staff Forward Deployed Engineer, AI/ML — Digitalocean98 · San Francisco