About the Job
Instead of accepting the status quo, we decided to fix it. National security professionals, journalists, parents, and everyone in between can stay connected and have privacy.
We didn’t just build a layer on top of old tech; we built America’s most private and secure mobile carrier from the ground up. By building our own network from scratch, we are able to design and build a suite of privacy and security features that no other carrier on the planet can offer.
Today, Cape provides our secure network to consumers, businesses, and government agencies alike. We closed our Series C in March 2026, and we are scaling rapidly, with the goal of giving people back control of their most personal information.
Key Responsibilities
Cape's engineering team is small by design and ambitious by nature. We build cryptographic primitives, 5G core network functions, zero-trust edge systems, and threat detection pipelines — problems that don't have StackOverflow answers. The people doing that work deserve an environment that gets out of their way.
As a Developer Experience Engineer, your job is to make Cape's engineers measurably faster, less frustrated, and more confident in what they ship. That means owning the internal platforms, tooling, and workflows that engineering depends on — from CI/CD pipelines and local development environments to AI-assisted development workflows, test infrastructure, and release processes. This is not a support role. It is a product role where the customer is the engineering organization, the product is the development loop, and quality is measured in time-to-merge, build reliability, AI-augmented velocity, and the number of times an engineer has to context-switch to deal with infrastructure instead of product.
You will work across the full engineering organization — embedded closely with platform, product, and security teams — and report to engineering leadership. Your impact will be felt by every engineer at Cape.
Build and maintain the internal tooling, scripts, and SDKs that Cape engineers use daily — from project scaffolding and local dev setup to testing utilities and debugging aids
Own the local development environment story: containerized setups, secrets management, service dependencies, and onboarding time for new engineers
Evaluate and introduce new tools thoughtfully — instrument adoption, gather feedback, and deprecate what isn't working
Write the documentation and runbooks that make complex infrastructure accessible to engineers who shouldn't need to become infrastructure experts to use it
Design and implement infrastructure that enables engineers to go from code to deployed artifact quickly and with high confidence, regardless of which product surface they're working on
Drive reliability improvements that eliminate common failure modes: flaky tests, non-reproducible builds, slow pipelines, and environment drift between local and CI
Own Cape’s AI-assisted development strategy — evaluate, integrate, and configure AI coding tools (code completion, automated review, agentic coding assistants) so engineers get high-quality suggestions that respect our security and compliance requirements
Build guardrails and context systems that make AI tools effective in Cape’s codebase — prompt engineering for internal tooling, retrieval-augmented generation over internal docs, and repository-aware configurations that keep AI suggestions relevant and safe
Measure the productivity impact of AI tooling rigorously — track adoption, code acceptance rates, time savings, and quality metrics to separate hype from genuine acceleration
Stay ahead of the rapidly evolving AI development tooling landscape and bring informed recommendations to engineering leadership on what to adopt, what to build internally, and what to avoid
Required Skills & Abilities
Experience building developer platforms in security-constrained environments (FedRAMP, DISA STIGs, or government compliance frameworks)
Familiarity with software supply chain security: artifact signing (Sigstore, Cosign), SBOM generation, dependency auditing
Experience at a high-growth startup where the engineering team scaled rapidly and tooling had to keep pace
Background in observability tooling: distributed tracing, metrics pipelines, and making complex systems legible to engineers
Experience integrating AI coding assistants (GitHub Copilot, Claude Code, Cursor, or similar) into engineering workflows, including prompt tuning, context management, and measuring productivity impact
Active U.S. security clearance or eligibility and willingness to obtain one
