AI Research Engineer

• A suite of analytics and observability tools, including product analytics, replay vision, AI observability, and error tracking

We are:

1. Product-led. More than 500K organizations have installed PostHog, mostly driven by word-of-mouth. We have intensely strong product-market fit.

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.

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 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 (rather than writing code).

Job Requirements

• A PhD is a plus, but a strong background in math is a must, or you have previous experience training models (strong math skills are still required).

• Strong PyTorch experience.

• Strong ability to code in Rust, CUDA, or C (with a preference for Rust).

• Solid understanding of Transformers (not the movies).

• A product mindset. you’ll be building things!

• This is a unique role within PostHog and, as such, is a hybrid-remote position based out of our London office.

What we’re not looking for

• Pure theoretical expertise: this is not a Jupyter notebook job.

• Someone who just wants to do research and publish. Yes, you can publish, but shipping takes priority.

We are committed to ensuring a fair and accessible interview process. If you need any accommodations or adjustments, please let us know

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