Full Stack Product Engineer

You do not need equal depth everywhere. You do need to own the whole result.

At Metaview, full stack does not mean collecting framework keywords. It means one engineer can follow a customer problem across the seams: from the interface to the API, data model, permissions, agent behavior, failure recovery, and production experience.

You may be exceptional on one side of the stack. That is useful here. What matters is that you do not treat the other side as someone else’s problem when it stands between you and a great product.

Metaview is an applied AI lab building AI coworkers for some of the world’s most ambitious recruiting teams. Hear directly from our customers. The challenge is not another chatbot. It is software that can pursue a hiring objective, act across real workflows, ask for judgment at the right moment, and earn a customer’s trust.

Founded by Siadhal Magos and Shahriar Tajbakhsh, who scaled Uber and Palantir, we have raised more than $50M and are growing 5x year over year.

AI coding agents are part of the job, not a perk. Engineers routinely run several in parallel for exploration, implementation, and review. We assess both sides of the craft: how well you direct agents and how well you reason, code, and debug on your own. One interview exercise is completed without AI.

Velocity means reducing the time from customer evidence to a reliable product change. It does not mean trading away quality. We ship small changes daily, including Fridays, because the engineer who ships owns production behavior, recovery, and the next revision. Process stays light because customer context and ownership are direct.

There is no queue of tickets moving between specialists. You will talk to customers, decide what product primitive solves the problem, build the visible experience and the systems behind it, then stay with the result in production.

AI agents help our small team ship roughly 8x more product changes than a year ago. The engineer remains accountable for architecture, review, implementation quality, and production behavior.

You will work on problems such as:

• building the interface, APIs, data model, permissions, and production behavior of the same customer workflow;

• building agents that can pursue recruiting objectives over days, know when to ask for judgment, and recover safely when a model or tool fails;

• turning interview and hiring-team conversations into actions, with interfaces that let recruiters understand, correct, and trust the result;

• designing controls and recovery paths for agents acting in customer systems; and

• turning repeated product patterns into simple primitives that support many recruiting workflows.

In your first 3 to 6 months, you should independently own and ship a bounded customer outcome from problem definition through rollout and iteration. The size of that outcome will reflect your level, but the operating model is the same: make product and technical decisions, contribute the consequential code, and own the production result.

• You have personally shipped software for external users and owned a meaningful outcome from problem definition through production.

• You have real depth in at least one technical area and can cross product, frontend, and backend boundaries when the outcome requires it.

• You use AI coding agents heavily and can still design, implement, review, and debug independently.

• You want direct customer contact, hands‑on implementation, and responsibility after launch.

• You enjoy following one product behavior through interface, API, data, permissions, and production recovery.

• You move toward the difficult layer rather than…