AI Research Engineer

We're shipping every product that companies need to run their business from their first day, to the day they IPO, and beyond. The operating system for folks who build software.

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:

• A built-in data warehouse, so users can query product and customer data together using custom SQL insights.

• A customer data platform, so they can send their data wherever they need with ease.

• PostHog AI, an AI-powered analyst that answers product questions, helps users find useful session recordings, and writes custom SQL queries.

Next on the roadmap are CRM, Workflow, revenue analytics, and support products. When we say every product that companies need to run their business, we really mean it!

We are:

1. Product-led. More than 100,000 companies have installed PostHog, mostly driven by word-of-mouth. We have intensely strong product-market fit.

2. Default alive. Revenue is growing 10% MoM on average, and we're very efficient. We raise money to push ambition and grow faster, not to keep the lights on.

3. Well-funded. We've raised more than $100m 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.

• 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.


PostHog is working on self driving product and is training its own deep ML models for this. Instead of telling Claude “build me X”, we want X to already have been built for you. You will be a substantial part in making this happen.


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.

This is an exceptionally large amount of data relative to our company size. For you, this and the fact that this team is new, means that you will have autonomy, be close to customers, and greatly influence what we actually ship.

On the finance side, we have around $150M in balance, we’re super strongly default alive and have had a rapidly growing, real business that makes good margins, runs exceptionally efficiently, and has already been scaling rapidly, for 6 years.


Areas we’re exploring

• Session replay analysis:
We already detect issues in replays, but it’s expensive and doesn’t scale. With better models (and a huge amount of DOM mutation data), we think we can make this faster, cheaper, and best-in-class.

• User behavior prediction:
Identifying drop-offs, suggesting improvements, and potentially improving conversion rates automatically — without heavy manual analysis or agent overhead.

• Synthetic user testing: 
Simulating users to catch confusing flows or breakages before release. If this works, it could remove one of the biggest bottlenecks in modern engineering: testing (not writing code anymore).

• You will need either a strong background in maths (and able to code) or to have worked on training models previously.

• We also require knowledge of Pytorch. Knowledge of CUDA and low level programming would be nice to have.

If you have a disability, please let us know if there's any way we can make the interview process better for you - we're happy to accommodate!