AI/ML Product Engineer

This is a hands-on, individual-contributor role with real product impact. You'll take problems from messy raw data all the way to a shipped, customer-facing insight, working directly with product, engineering, security and the customers themselves.

Responsibilities

Turn raw AI usage data into insights customers can see, trust and act on; Own insight features end to end, from framing the question with users and product partners through to production and iteration; Make sense of unstructured data at scale, with the quality of every output measured and understood; Build and maintain the data pipelines behind your models, and own their deployment and reliability in production; Build the evaluation frameworks, ground-truth sets and error analysis that prove an insight holds up before a customer sees it; Quantify uncertainty and communicate confidence clearly, so the product never presents a shaky signal as a certain one; Shape the product surface itself: what the model should show, to whom, and why; Keep pace with a fast-moving field, adopting new models and approaches as they prove themselves and retiring the ones that don't; Work across a polyglot stack: Python at the core, with Java (Spring Boot) and occasionally TypeScript when the task demands it, deployed on AWS

Requirements

A track record of shipping production ML that customers or downstream product surfaces depend on, such as text or behaviour classifiers, or clustering and embedding pipelines; Solid applied ML foundations across supervised classification and unsupervised grouping, with a clear view of which model families you've used and why; Strong production Python for ML and data engineering, with code in version control, tested and deployed; Rigour about validity, routinely reporting precision and recall, confidence intervals and baselines, and having run experiments or human-in-the-loop review to verify automated conclusions; Experience delivering ML as part of a product team on a customer-visible surface, ideally having owned an insights, analytics or intelligence feature; A quantitative background (statistics, CS, physics, econometrics or equivalent) applied to real data problems; Comfort in a polyglot codebase and a willingness to pick up a second language on the job

Nice to have

Have built products in cybersecurity, AI detection, data protection, governance or enterprise SaaS, or have a real interest in how organisations adopt AI safely; Have worked on taxonomy design, data labelling, or entity and intent categorisation in a product context; Have been an early or founding engineer or data scientist and defined the product surface yourself; Care whether a signal in the data is real, and will argue about sampling bias and statistical validity when it matters; Have translated messy data into dashboards, scores or narratives that buyers rely on; Want a senior IC seat with build-it-yourself scope, not a step onto a management ladder; Thrive in early-stage ambiguity and prefer small, fast product companies to large platform teams; Leverage AI tools as an engineer to help you build smarter, faster and better

Benefits

Competitive pay and meaningful equity; Comprehensive benefits including pension plan and flexible hybrid work; Annual global off-sites; Real ownership over product, culture and direction of the company