Overview

Hi, I'm Aakash, Director of Engineering at Headout. I'm looking for a Staff Engineer, Web to shape how Headout's consumer teams design, build and learn in an AI-native world. You will stay close to hard web engineering problems while setting patterns that other teams can use, not simply advise from the sidelines.

The interesting challenge is not getting an agent to produce a page. It is making sure the page respects the design language, behaves well in production and teaches us something when it changes. I want someone who can make that work verifiable, and who can turn good local solutions into useful shared tools without imposing a platform for its own sake.

Why this role, why now

Headout's consumer teams work like a lab: experiments tell us what is worth building further and what should be shelved. AI has made it faster to build, but that speed has not yet translated into equally fast, trustworthy experimentation. The opportunity is to make design, development, verification and measurement work together, so humans and agents can safely propose, run and learn from product changes.

What you'll own

• Design-system adherence for agent-built work. Define the contracts and engineering checks that help agents build within an established design language, whether extending an existing experience or launching something new. Make exceptions deliberate, visible and manageable.
• Verification that earns trust. Build evaluation and review loops that catch real defects and protect design fidelity. Connect checks before release with what actually happens in production, rather than rewarding teams for passing a suite that misses the user experience.
• Experimentation as a usable platform. Shape the infrastructure that lets people and agents propose experiments, run them with controlled audiences, measure results and learn. Treat measurement, safety and the ability to stop a bad change as part of the design.
• Shared architecture across consumer pods. Spot where repeated solutions should become a common abstraction and where teams need room to differ. Help teams adopt the right shared patterns through clear contracts and practical migration paths.
• Hands-on technical leadership. Get into complex web problems with teams, test solution paths in code, and help engineers make stronger decisions. The work spans deep engineering judgment and a longer-term view of how design and development should collaborate with agents.

You could be a great fit if

• You have at least 9 years of engineering experience, with substantial hands-on depth in consumer web engineering, including React and TypeScript, and can reason about rendering, performance, resilience and production trade-offs.
• You have led technical work across teams without relying on reporting lines. You know when a shared abstraction helps, and you can earn adoption rather than mandate it.
• You have built or governed design systems, quality gates, experimentation or measurement infrastructure, and can explain how you knew they worked.
• You are curious about AI-assisted engineering but insist on evidence, guardrails and clear failure modes. You can work through ambiguity while keeping your standards high.

You may not enjoy this role if

• You prefer a purely advisory architecture role and do not want to join difficult work in the code.
• You are satisfied with prompt instructions alone when an agent's output needs enforceable checks.
• You want to build a shared platform without investing in adoption, measurement and migration.

Interview process

We want you to see the real problems and have room to ask questions throughout.

• Recruiter conversation. We discuss your web depth, cross-team work and interest in the charter.
• Technical deep dive. We explore design-system guardrails, experimentation and the choices involved in building a platform for several teams.
• Live build. Show how you work with AI to solve a web and design-system problem, showing how you direct and verify its work. Setup details will be provided in advance.
• Web system design. We work through a consumer experience, including performance, reliability, experimentation and release.
• Leadership conversation. We discuss technical vision, influence across teams and how you raise standards.