Engineering Manager

Lumbra builds the vendor-neutral operating system for agentic AI in high-consequence environments. In our first ten months we won four U.S. Government prime contracts in open competition, every one deploying into classified spaces, and we are live on JWICS. We were selected from a field of more than 200 companies, beat two established defense primes head to head, and earned the follow-on, now in execution. Lumbra is a team of engineers and operators from the intelligence community, special operations, and frontier AI research, trained at MIT, Carnegie Mellon, West Point, and U.S. Army Ranger School, with careers built at Palantir, Anduril, Northrop Grumman, Onebrief, JSOC, NSA, and CIA. We have lived this problem from the inside.

Nebula is our agentic harness, and it runs in production on air-gapped, classified networks. The Core Platform team owns the full stack, from backend services and frontend through workflow orchestration to the infrastructure that has to behave the same in commercial cloud and inside a SCIF. We want an engineering manager who can lead that team through fast iteration without letting the bar on architecture or reliability slip.

• Lead a cross-functional team across backend, frontend, and infrastructure. You carry enough technical depth to review architecture decisions and unblock engineers anywhere in the stack.

• Own the product and engineering roadmap for the core platform, weighing new capability against reliability and tech debt in an AI product where the ground keeps shifting.

• Set the engineering culture: how the team tests, shapes its architecture patterns, runs design reviews, and holds the line on code quality, enforced through review and mentorship rather than policy.

• Own the release lifecycle from development to production, diagnosing pipeline bottlenecks and managing release risk.

• Own production monitoring, incident response, and post-incident reviews, with a recurring reliability cadence that holds the team accountable for platform health.

• Coordinate initiatives that span teams and deployment targets, including the classified, air-gapped environments where you own cross-environment parity and the details that quietly break it.

• Build the hiring pipeline: find the skill gaps, define roles, run interviews, and ramp new engineers into a complex, fast-moving codebase.

• Has led engineers through planning, delivery, and growth, including a team of five or more

• Builds software in Python and TypeScript, with hands-on async web work

• Came up as a senior or staff engineer and can still spot an architecture problem in a diff

• Has steered technical direction in a fast-moving AI product

• Knows container orchestration (Kubernetes), cloud infrastructure such as GCP, and CI/CD design

• Managed teams building AI or ML products and understands non-deterministic systems, evaluation, and model integration

• Shipped to air-gapped or classified environments and carried the compliance overhead

• Scaled an engineering team from five to fifteen or more

• Comes from intelligence, defense, or analytical tooling and feels the end user's problem