AI Research Engineer

About PostHog Product development used to mean manually writing code, running analysis, diagnosing bugs, and rolling out changes using dozens of tools. PostHog is the only platform that acts like a co-pilot for you (and your AI agents) to do it all – autonomously. We started with open-source product analytics, launched out of Y Combinator's W20 cohort . We've since shipped more than a dozen products , including: PostHog Code , the only AI devtool that understands your product, not just your codebase. A built-in data warehouse , so users can query product and customer data together using custom SQL insights. PostHog AI , an AI-powered analyst that answers product questions, helps users find useful session recordings, and writes custom SQL queries. We are: Product-led . More than 450,000 organizations have installed PostHog, mostly driven by word-of-mouth. We have intensely strong product-market fit. Default alive . Revenue is growing incredibly quickly, and we're very efficient. We raise money to push ambition and grow faster, not to keep the lights on. Well-funded. We've raised more than $180m from some of the world's top investors. We're set up for a long, ambitious journey. We're focused on building an awesome product for end users, hiring exceptional teammates, shipping fast, and being as weird as possible . Things we care about Transparency: Everyone can read about our roadmap, how we pay (or even let go of) people, our strategy, and how we work, in our public company handbook . Internally, we share revenue, notes and slides from board meetings, and fundraising plans, so everyone has the context they need to make good decisions. Autonomy: We don’t tell anyone what to do. Everyone chooses what to work on next based on what's going to have the biggest impact on our customers, and what they find interesting and motivating to work on. Engineers lead product teams and make product decisions . Teams are flexible and easy to change when needed. Shipping fast: Why not now? We want to build a lot of products; we can't do that shipping at a normal pace. We've built the company around small teams – autonomous, highly-efficient groups of cracked engineers who can outship much larger companies because they own their products end-to-end. Time for building: Nothing gets shipped in a meeting. We're a natively remote company. We default to async communication – PRs > Issues > Slack. Tuesdays and Thursdays are meeting-free days , and we prioritize heads down building time over perfect coordination. This will be the most productive job you've ever had. Ambition: We want to solve big problems. We strongly believe that aiming for the best possible upside, and sometimes missing, is better than never trying. We're optimistic about what's possible and our ability to get there. Being weird: Weird means redesigning an already world-class website for the 5th time. It means shipping literally every product that relates to customer data. It means building an objectively unnecessary developer toy with dubious shareholder value. Doing weird stuff is a competitive advantage. And it's fun. Who we're looking for PostHog is working on a self-driving product and is training its own Deep ML models. To do this, we have a new AI Research team. We track more products across more dimensions than any other company on Earth. Because of this, we have petabytes of data across events, errors, session replays, revenue data, and more. It’s an exceptionally large amount of data relative to our company size, and we want to use it for our users' benefit. Just imagine how, with the vast amount of data we capture, we can automatically surface insights and suggest fixes. As an AI Research Engineer, you will play a substantial part in making this happen. This team is brand new, so you will have real autonomy, be close to customers, and greatly influence what we actually ship. On the finance side, we have around $150M on our balance sheet, we’re "default alive," and we have a real business that has been scaling rapidly and running exceptionally efficiently with good margins for six years. We invest in what we believe moves the needle. What you'll be doing You'll be part of a team that researches what's next and what's better, and actually brings it into production. Areas we’re exploring Session replay analysis: We already detect issues in replays, but doing so is expensive and doesn’t scale. With better models (and a huge amount of DOM mutation data), we think we can