Member of Technical Staff

We’re building an AI‑native workspace—an operating system for work that puts co‑intelligence at the center. Start with data or an idea, describe your goal, and Obvious goes to work: running analysis, searching the web, writing documents, generating tables, designing presentations, visualizing data, building dashboards, and more.

As Steve Jobs imagined the personal computer as a bicycle for the mind, Obvious imagines AI as a garden for the mind. Less mechanical acceleration. More organic cultivation.

What if, instead of just vibe coding, you could vibe‑work? What if getting from idea to done wasn’t so opaque, stubborn, and high‑latency?

What if there was a way to consistently deliver work that feels like it came from the best version of you on your best day?

We're building an AI-native workspace—an operating system for work that puts co-intelligence at the center. Start with data or an idea, describe your goal, and Obvious goes to work: running analysis, searching the web, writing documents, generating tables, designing presentations, visualizing data, building dashboards, and more.

As Steve Jobs imagined the personal computer as a bicycle for the mind, Obvious imagines AI as a garden for the mind. Less mechanical acceleration. More organic cultivation.

What if, instead of just vibe coding, you could vibe-work? What if getting from idea to done wasn't so opaque, stubborn, and high-latency?

What if there was a way to consistently deliver work that feels like it came from the best version of you on your best day?

That's Obvious.

While we've made significant progress with our AI features like search, data enrichment, modes, artifact generation, coding, and more, we're just scratching the surface of what's possible.

We need engineers who can bridge the gap between powerful AI models and production-ready experiences that solve real user problems.

This isn't just about writing prompts – it's about building resilient systems that can handle the unique challenges of working with AI at scale and can solve some of the industry's most interesting problems like memory, agent-to-agent collaboration, non-deterministic repeatability, and more.

We are small and talent-dense. Among our founding team, we have world-class builders, former founders, and leaders from companies like Netflix, Google, Uber, Meta, Dropbox, Instacart, Shopify, Apple, Datadog and Twitter (X). If you're excited to solve some of the world's most challenging problems and build Al that can deliver on real-world objectives, join us.

We've made real progress on the AI features that make Obvious work—search, data enrichment, modes, artifact generation, coding—and we're still only scratching the surface of what's possible.

This is a generalist product engineering seat, firmly in the application space. We're looking for someone who translates customer problems into the most elegant, cost-efficient solution—not someone chasing flashy tech for its own sake. That means bridging the gap between powerful AI models and production-ready experiences that solve real user problems. This isn't about writing prompts. It's about building agent-facing UX and the resilient systems behind it: memory, agent-to-agent collaboration, non-deterministic repeatability.

We are small and talent-dense. Among our founding team, we have world-class builders, former founders, and leaders from companies like Netflix, Google, Uber, Meta, Dropbox, Instacart, Shopify, Apple, Datadog, and Twitter (X). If you're excited to solve some of the industry's hardest problems and build AI that delivers on real-world objectives, join us.

• Ship at least 3 PRs on your first day

• Drive full-stack feature development from conception to deployment, owning key product initiatives end-to-end

• Collaborate on the design and implementation of user-facing features that improve the experience

• Build robust, performant web applications using modern frontend and backend technologies

• Identify opportunities for optimization and enhancement in existing systems

• Contribute to the architectural decisions that shape the product

• Set the industry standard for the UX of agent products

• Build AI/LLM integrations as a core part of the product, not a bolt-on—agent-facing UX, not just prompting

• Genuine full-stack range, frontend-leaning: strong TypeScript/React paired with real API and backend chops—not a single narrow lane

• AI/LLM integration experience you can point to, not just talk about—production work building agent-facing features, ideally with the eval harnesses or quality feedback loops behind them

• An engineering bar we hold at staff level regardless of how you're titled—we'll ask about design-system fluency and client-server/UI performance judgment, not just ship velocity

• A track record of end-to-end ownership—driving ambiguous problems from first idea to shipped, not executing a spec handed down

• Strong problem-solving skills and attention to detail

• Excellent communication skills and the ability to work cross-functionally

• Willingness to work hard, move fast, and grow quickly in a rapidly changing environment

• A humble, team-first attitude and a desire to do whatever it takes to make the team succeed

• Background in coding agents or multi-agent systems

• Informed opinions on agent orchestration

• Contributions to open-source projects or developer tools

• Previous experience at a high-growth startup

• You need fully-specced requirements to operate—at our size, you'll define scope as often as you're handed it

• You see frontend and backend as separate lanes rather than one job

• You can't handle startup pace

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