Obvious · Los Angeles
OtherEngineeringRemoteFullTimeposted 7w ago
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'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.
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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