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About the Role
We're looking for an AI/ML Product Engineer to build the insight layer of Harmonic: the ML that makes sense of how people and agents are actually using AI, so our customers can see where it's creating value and where it's creating risk. 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. This is about turning AI usage into insight that customers act on. Classification and clustering are how we do that today, but you'll choose whatever approach fits the problem. It's Python-first engineering, not notebook analysis.
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; this role isn't a fit for a background built mainly on forecasting or time-series prediction
- Strong production Python for ML and data engineering – code in version control, tested and deployed, not just explored
- Rigour about validity: you routinely report precision and recall, confidence intervals and baselines, and you've 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 yourself
- 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
- 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
Skills
- Python
- Java
- Spring Boot
- TypeScript
- AWS
- Classification
- Clustering
- Supervised learning
- Unsupervised learning
- Data engineering
- MLOps
Location
- Shoreditch
- Remote
Work Type
- Hybrid
- Remote
Experience Level
- Senior
Education Level
- Quantitative background (statistics, CS, physics, econometrics or equivalent)
Benefits
- Pension plan
- Flexible hybrid work
- Competitive pay
- Meaningful equity
About the Company
- Harmonic Security governs AI, understanding user/agent intent and data context in real time to provide security teams visibility and control across tools and workflows. They enable companies to move faster with AI while minimizing risk.
- They operate at the intersection of AI and cybersecurity, building solutions to protect sensitive data in real time with minimal friction.
- Harmonic was named to the Rising in Cyber 2026 list and is backed by N47, Ten Eleven Ventures, and In-Q-Tel.
- Their AI-First by Design approach means everyone leverages AI tools to perform their best work.
- The Product Delivery team ships early and often, working in the open and trusting each other to own outcomes.
- Harmonic's Core Values include Flourishing in the Unknown, Never Full, and Perfect Harmony.