Design Engineer

AI Gateway gives developers one place to connect applications and agents to hundreds of models with one API key. It handles provider routing and fallbacks, unifies billing and observability, and lets teams manage spend and security controls across production AI workloads.

Designing this well is a systems problem. A developer should be able to choose the right model, make and verify a first request, diagnose a failed request, and configure routing, budgets, credentials, and security controls without losing sight of the application they are trying to build. The right interface might be in the dashboard, on the public model catalog, in the CLI, in an API response, or in code.

You will be the Design Engineer for AI Gateway, owning product design and frontend implementation across those surfaces. You will work with customers, a Product Manager, and engineers to understand the problem, explore solutions, build the production experience, ship it, and learn from what happens after launch.

AI Gateway is live, the model ecosystem changes every week, and the team can move from an observed problem to production quickly. This is a rare chance to use your product judgment, design craft, and code to shape a developer product and ship the experience yourself.

You will be the design owner embedded with a Product Manager and a dedicated engineering team. You will also have the Product Design team around you for critique, collaboration, and support. AI Gateway will be your main product area, with room to pursue design-led improvements across Vercel. You will report to the Design Team.

If you’re based within a pre-determined commuting distance of one of our offices (SF, NY, London, or Berlin), the role includes in-office anchor days on Monday, Tuesday, and Friday. If you're located beyond that distance, the role is fully remote. For location-specific details, please connect with our recruiting team.

• Own product design and frontend implementation for meaningful parts of the journey from discovering a model to making a first successful request and operating AI in production.
• Design and build production experiences across the website, dashboard, CLI, and APIs, choosing the right medium for each problem.
• Make models, providers, credentials, routing, budgets, logs, performance, privacy, and governance understandable, then create patterns that extend across new providers, modalities, and workloads.
• Talk directly with customers and use product data to understand problems, define success, make tradeoffs, and improve what ships.
• Own quality after launch, including visual and interaction craft, accessibility, performance, resilience, instrumentation, and edge cases.
• Use coding agents to explore and implement ideas quickly, while testing, debugging, and taking responsibility for what ships.