Staff Engineer, Hightouch

• You are an engineer with a passion for solving hard technical problems that generate real value for customers
• You’re motivated by high ownership and are comfortable in a fast-paced, startup environment
• Strong software engineering fundamentals—you can dive into complex backend code and understand it quickly
• Deep hands-on experience with AI coding agents (Claude Code, Cursor, Copilot, Devin, or similar)
• Experience writing effective agent documentation, custom instructions, or context files that meaningfully improved agent output
• Track record of building developer tools or infrastructure that other engineers actually use
• Comfortable working across the stack: you'll touch CI/CD, cloud infrastructure, internal tooling, and application code

Desirable

• We are looking for a Staff AI Productivity Engineer to accelerate how our engineering organization works with AI coding tools
• This is a hands-on technical role focused on building infrastructure, tooling, and documentation that makes AI agents dramatically more effective across our codebase
• AI tools are evolving rapidly, and we believe the companies that invest in making agents truly productive - not just available - will have a significant advantage
• You'll own this problem end-to-end: from setting up cloud development environments where agents can run autonomously, to building MCP integrations that give agents access to our internal systems, to creating the documentation and context that helps agents understand how we build software
• You’ll have the hands-on acumen to do a lot of the heavy lifting yourself and the leadership skills to drive cross-team efforts to raise the bar everywhere
• This role is a companion role to our Developer Productivity role but has a distinct focus: while Dev Productivity owns the build/test/deploy pipeline, you'll own the AI-assisted development experience
• An exceptional candidate could wear both hats
• Own the agentic development environment: Ensure agents can operate in independent cloud-based development environments, execute our full test suites, examine build results visually, etc
• Build our tooling integrations: Build MCP server integrations that connect our agents to the systems needed to build and debug software, such as CircleCI, Slack, Datadog, Github, etc
• Documentation and context: Own our repo-wide agents.md file and work with teams to ensure our library of agent guidance and skills is continually pushing the bar. Ensure our conventions and package structures are exposed in ways that agents can effectively use
• Enablement: Work with our engineers to understand where agents are struggling and address root causes such as better docs, tooling access, etc. Develop tooling for non-engineers to make simple visual updates to our application
• Serve as a “PM” for internal AI agents: Consistently keep us on the leading edge of AI productivity trends by staying abreast of what is state-of-the-art in industry and driving those improvements across Hightouch

• Our goal with the interview process is to balance speed with giving both parties opportunities to assess whether there is a strong mutual fit. We will ask you questions, but we want you to ask us questions! Our technical interviews focus on how you design systems because we believe this is the best way for us to see how you work and for you to see how we collaborate. We don’t ask you to write code to solve technical brainteasers that don’t appear in your day to day job
• Recruiter Screen [30m]: Introductory call with our recruiting team to get to know each other and see if the role could be a good mutual fit
• System Design Screen [45m]: Designing a data processing feature end-to-end
• AI Skills Interview [60m]: We’ll dive into how you leverage AI and how you’ve helped others do the same
• Hiring Manager Interview [45m]: Chat with hiring manager about past experiences and future operating preferences to assess fit on company values and operating principles
• System Design Interview [90m]: Work with the interviewer to architect a system at a conceptual level. The problem will be at a pretty high level - and have both product and customer requirements as well as technical