AI/ML Product Engineer (Cybersecurity)

Location: Hybrid in the Shoreditch office around two days a week; remote work in the United Kingdom is available, with remote consideration for exceptional candidates

Company

hirify.global builds an AI Governance and Control platform that gives enterprises real-time visibility and control over AI usage, data, and risk.

What you will do

• Turn raw AI usage data into customer-facing insights that support decisions about value and risk.
• Own insight features end to end, from framing problems with users and product partners through production delivery and iteration.
• Build and maintain data pipelines, model deployments, and production reliability for machine-learning features.
• Develop evaluation frameworks, ground-truth datasets, error analysis, and human-in-the-loop validation.
• Quantify uncertainty and communicate model confidence clearly in the product.
• Work across Python, Java with Spring Boot, TypeScript, and AWS while adopting effective new models and approaches.

Requirements

• Experience shipping production machine learning used by customers or downstream product surfaces, including classifiers, clustering, or embedding pipelines.
• Strong applied ML foundations in supervised classification and unsupervised grouping; forecasting and time-series prediction should not be the primary background.
• Strong production Python for machine learning and data engineering, including version-controlled, tested, and deployed code.
• Experience measuring precision, recall, confidence intervals, and baselines, and validating automated conclusions through experiments or human review.
• Experience delivering machine learning within a product team on a customer-visible surface.
• Quantitative training in statistics, computer science, physics, econometrics, or an equivalent field, plus comfort working in a polyglot codebase.

Nice to have

• Experience with cybersecurity, AI detection, data protection, governance, or enterprise SaaS.
• Experience with taxonomy design, data labelling, entity categorisation, or intent categorisation.
• Background as an early or founding engineer or data scientist who defined product surfaces.
• Experience translating messy data into dashboards, scores, or narratives that buyers rely on.
• Experience in early-stage product companies and interest in using AI tools to improve engineering work.

Culture & Benefits

• Small, high-trust team focused on transparency, creativity, continuous learning, and direct ownership.
• Competitive pay, meaningful equity, pension plan, and flexible hybrid work.
• Annual global off-sites, with previous trips including Lisbon and Nashville.
• Environment that values initiative, rapid decision-making, collaboration, and candid feedback.
• Opportunity to shape an emerging AI and cybersecurity product category from an early stage.