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PostHog is seeking an engineer who can train and ship machine learning models into a real product. You will work on end-to-end model development, from research to production, including scalable inference and user-facing features.
The role requires strong math, PyTorch proficiency, and comfort writing low level code in Rust, CUDA, or C. You will collaborate closely with customers on a brand-new team to understand problems, validate models, and decide what is good enough to ship.
The role is for an engineer who can train and ship machine learning models, not only experiment with them. It calls for strong math, solid PyTorch skills, comfort writing low level code in Rust, CUDA or C, and a good grasp of how Transformers work. A strong candidate cares about getting results into a real product and is happy to work closely with customers on a brand new team.
The work is about building the models that sit behind a product that finds problems and suggests fixes on its own. AI takes over slow, costly analysis of replays and user behavior, and can simulate users to test flows. What stays human is choosing which problems are worth solving, judging whether a model's output is actually useful, and deciding what is good enough to ship.
Someone in this role would train and deploy their own deep models on very large sets of real product data, including session replays, events and errors. That is hands on experience that few teams offer. To prepare, build a small project that trains a Transformer on sequence data such as click streams or page changes, and take it as far as a working demo.
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
All-in-one product analytics, session replay, and feature management platform for engineering teams.