Software Engineer, AI Enablement
New
T
TailscaleSoftware Engineering
Tailscale is fully distributed and we collaborate in person with teammates across Canada, the United States, the United Kingdom, and Singapore.Full-TimeSenior
Salary218,420 - 302,840 CAD per year
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Job Details
- Experience
- 5+ years of professional software engineering experience
- Required Skills
- PythonGoRustSoftware Engineering
Requirements
- 5+ years of professional software engineering experience.
- Proven hands-on, daily fluency with modern AI coding tools (e.g., Claude Code, Codex, Pi).
- Experience building and shipping internal tools or developer-facing automation.
- Strong written and verbal communication and internal advocacy skills.
- Ability to influence engineers across varying comfort levels with AI tooling.
- Demonstrated product or systems thinking with an ability to identify and prioritize high-leverage opportunities.
- Comfort with ambiguity and self-directed prioritization on a small team.
- Experience in developer experience, platform engineering, or internal tooling is a plus.
- Familiarity with evaluating or benchmarking LLMs and coding agents is a plus.
- Proficiency in a systems language such as Go or Rust, in addition to Python, is preferred.
- Background in security or code-review practices is a plus.
- Experience in technical evangelism or developer relations is considered a plus.
Responsibilities
- Partner with engineering leadership and technical staff to define internal AI-enablement priorities and roadmap.
- Contribute to the development of Aperture by Tailscale.
- Build, maintain, and iterate on internal tools, workflows, and best practices for using coding agents like Claude Code, Codex, and OpenCode.
- Evaluate new AI models and tools to provide actionable adoption recommendations.
- Collaborate across engineering and non-engineering teams to identify and implement high-leverage AI opportunities.
- Establish infrastructure, security guardrails, and review practices for AI-generated code.
- Advocate for effective AI practices through documentation, internal talks, and coaching.
- Track and report on the impact of AI tooling on engineering velocity and quality.
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