Product Engineer, Foundernest

• We are not hiring for a specific seniority. What matters most is how you think and how you work with other people, but we require candidates to have at least ~2 years of professional experience building and shipping software. Beyond that we are open
• From an engineering perspective, you are either full-stack or strong on the backend. Most of this work (integrations with APIs and SDKs, data pipelines, LLMs) lives on the backend, so that is where we need you to be solid. Knowing your way around the frontend, or being genuinely up for learning it, is a plus
• From a product perspective, you take ownership of problems, not just tasks. You want to know who the user is and what actually helps them before you start building. Here your users are a Slack message away, so there is no excuse not to ask them
• From a mindset perspective, you are lean and pragmatic. You would rather ship something small and useful this week than something perfect next quarter. You choose solutions proportional to the problem, and you can explain the tradeoffs you made
• From a collaboration perspective, you can work with people who are not engineers, explaining your reasoning without jargon, and taking a half-formed request from someone in sales and turning it into a real problem statement
• You disagree constructively and hold strong opinions weakly. Low ego, high curiosity
• From a learning perspective, you pick things up fast and you enjoy doing it. Most of what this role needs you can learn here, but that only works if learning is something you are genuinely good at

Desirable

• You would be joining our GTM Engineering team
• This team's job is to unlock revenue by taking the repetitive, mechanical work out of Sales, Marketing and Customer Success, so that the people in those teams spend their time on the things only a human can do: closing deals, nurturing prospects, and building the kind of customer relationships that turn into upsells and expansions
• We do that with systems, automation and a lot of experimentation. We are scrappy by design: find the shortest path to something that works, put it in front of the people who need it, learn, and keep going
• When it works, the impact shows up in the company's numbers, not in a backlog
• The problems have the similar shape as the ones inside our product: LLMs, large and imperfect datasets, a lot of integration work, and real ambiguity about what should be built in the first place
• The difference is who it is for: the people you build for work here, which means you can walk up and ask them
• You would join a genuinely cross-functional team: your closest teammates are not only engineers, but people from product, marketing and sales. You sit with the problem and the people who have it, not at the receiving end of a spec
• Find where the most impactful friction is and build the thing that removes it: often that means spotting the manual, repetitive work someone is drowning in before they have thought to ask for help
• Build it end-to-end, mostly on the backend: integrations with the tools our teams work in, data pipelines, LLM-powered workflows, and whatever interface makes it usable
• Break ambiguous problems into simple solutions: shipping something valuable early and iterating from real feedback
• Own the outcome: what matters is whether the thing you built moved the number, not whether it shipped
• Work with LLMs and a lot of third-party APIs
• Our stack includes Typescript and Python, PostgreSQL and MongoDB, BigQuery and DBT, and infrastructure running on Kubernetes (AWS). On the AI side, we work with multiple LLM providers, RAG pipelines, internal agents, and evaluation systems
• You don’t need experience with all of this, but you should be comfortable navigating similar environments
• Most engineers never get to draw a straight line from something they built to money the company made. Here you can
• This team is measured in leads, meetings booked and revenue, so what you ship lands in numbers the whole company is watching, and you will know which ones you moved
• The feedback loop is unusually short, too. You ship something on Tuesday, someone is using it on Wednesday, and by Friday you know whether it worked. Very few engineering roles let you see the consequences of your work that fast
• You will own problems end-to-end on a small team, with real influence on what gets built and why. There is no layer of translation between you and the people you are building for
• And you will work alongside AI systems in production from day one, which is a rare thing to get to learn on the job

• Please include the url of your LinkedIn profile