What You'll Do
- Take clear ownership of rapid AI adoption across the engineering organization
- Identify high-friction areas in engineering workflows where AI can meaningfully improve productivity
- Design and build practical, production-grade AI-powered developer tooling (coding, testing, PR reviews, debugging)
- Build contextual, system-aware AI assistants using internal data, codebases, and tooling
- Explore, prototype, and productionize AI-driven solutions with strong autonomy on how problems are solved
- Automate and streamline workflows across GitLab, Jira, CI/CD, Slack, and observability tools
- Design and operate internal AI services and orchestration layers (e.g. MCP servers)
- Own solutions end-to-end: discovery → design → build → measure → iterate
- Work hands-on with engineering teams to remove friction, enable usage, and move tools from delivery to daily practice
- Measure success through adoption, impact, and tangible time saved for engineers
What You Won't Do
- Build AI features for customer-facing products
- Work on speculative AI research without clear outcomes
- Act as a general internal support team
- Own generic ML infrastructure unrelated to developer productivity
What We’re Looking For - Required Experience
- 5+ years of experience as a software engineer, with recent focus on GenAI systems
- Strong experience building production-grade systems, not just prototypes
- Hands-on experience with:
- LLMs (OpenAI, Anthropic, etc.)
- Prompting, retrieval, and context injection
- AI-powered tooling or internal platforms
- Solid backend engineering skills (APIs, services, integrations)
- Experience working with developer tools (CI/CD, GitHub/GitLab, Jira, observability)
- Strong product mindset and comfort operating in ambiguous problem spaces
Nice to Have
- Particularly interesting profiles are engineers who have built developer tools and are now evolving toward AI-native system design.
- Prior experience building developer tools, internal platforms, or DevEx tooling
- Experience evolving traditional tooling into AI-assisted or AI-driven workflows
- Familiarity with MCP, agent-based systems, or model orchestration concepts
- Experience integrating AI with large codebases, monorepos, or complex CI/CD environments
- Exposure to security, privacy, and trust considerations in internal AI systems
How You’ll Be Successful
- AI solutions you build are widely adopted and used regularly by engineers
- Engineering productivity measurably improves, using:
- existing metrics we already track (e.g. DevEx, CI, delivery, quality, flow), and/or
- new, clearly defined metrics you help introduce to capture AI impact
- Manual, repetitive workflows are reduced or eliminated, with clear before/after comparisons
- Engineering time is visibly saved and reinvested into higher-value work
- Improvements are demonstrated with data, not just qualitative feedback
- Adoption grows organically because tools are useful, fast, and well-integrated into existing workflows
Team & Environment
- You’ll join the Engineering Productivity team
- You’ll work closely with engineers across the company
- Strong collaboration with Infrastructure and Security teams
- Product-oriented culture focused on outcomes, not hype
Location
- United States (preferred: Seattle or San Francisco)
- Open to strong US-based candidates in other locations
- Collaboration with teams in Europe expected
