Software Engineer, AI Enablement

Key Responsibilities

• Partner with engineering leadership, our co-founder and members of technical staff to define Tailscale's internal AI-enablement priorities and roadmap.
• Work with the team that’s building Aperture by Tailscale – our AI gateway that we both use internally and provide as a product.
• Build, maintain, and iterate on internal tools, workflows, and shareable practices (skills, development environments, internal tooling) that help engineers use coding agents like Claude Code, Codex, OpenCode, and Pi more effectively.
• Evaluate new AI models, agents, and tools, and make clear recommendations on what to adopt and why.
• Work directly with both engineering and non-engineering teams across the company to find high-leverage opportunities to apply AI to their day-to-day work — this is a support/enablement function, not a standalone product team, so success means other teams shipping faster because of what you've built.
• Develop infrastructure, guardrails, review practices, and documentation for the safe, secure use of AI-generated code.
• Act as an internal advocate for effective AI practices — through documentation, internal talks, and hands-on coaching — meeting teams where they are rather than mandating a single tool or workflow.
• Track adoption and measure the real impact of AI tooling on engineering velocity and quality, and report back to leadership.
• Expect the day-to-day to span hands-on building, internal teaching/evangelism, and prioritization — on a team this size, you'll move between all three regularly.

Requirements

• 5+ years of professional software engineering experience
• Genuine, daily, hands-on fluency with modern AI coding tools (Claude Code, Codex, Pi, or similar) — real power-user experience, not occasional use
• Experience building and shipping internal tools or developer-facing automation
• Strong written and verbal communication and internal advocacy skills — comfortable teaching, writing, and influencing engineers at very different comfort levels with AI tooling
• A balanced approach — trying to get people to use AI effectively and safely where appropriate. We don't have an 'AI mandate'; we believe that if we make AI both easy to use and safe, people will choose to use it when appropriate
• Comfort with ambiguity and self-directed prioritization on a small, still-forming team
• Product or systems thinking — able to identify and prioritize high-leverage opportunities, not just execute a pre-set roadmap

Nice to Have

• Prior experience in developer experience, platform engineering, or internal tooling
• Experience evaluating or benchmarking LLMs and coding agents
• Experience with a systems language such as Go or Rust, in addition to Python
• Background in security or code-review practices — helpful for reasoning about AI/agent-generated code, though you don't need to be a security engineer
• Experience in developer relations, technical evangelism, or customer-facing developer advocacy — the same skills that make someone effective in external DevRel translate well to driving internal AI adoption
• Technical project management experience