Senior Machine Learning Engineer

🕒 February 16

🇬🇧 United Kingdom – Remote

💵 €106.1k - €169.8k / year

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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HubSpot

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2006

🤝 B2B

☁️ SaaS

B2B • SaaS • Marketing

HubSpot is an AI-powered customer platform that combines marketing, sales, and customer service software into one integrated suite. With over 238,000 customers in 135 countries, HubSpot offers tools for marketing automation, sales management, customer service, content marketing, operations, and B2B commerce. With products like Marketing Hub, Sales Hub, Service Hub, and Content Hub, HubSpot enables businesses to generate leads, close deals, and provide excellent customer support, all while using AI to enhance operations and insights. The platform is designed to unify teams and customer data, supporting both small startups and large enterprises in their growth journey.

📋 Description

• Design, build, and operate backend services that power context retrieval and enrichment for AI assistants and agents. • Build platform capabilities for storing, searching, and retrieving “insights” and relevant facts across HubSpot’s GTM data. • Develop systems to manage and compress context when it gets large (e.g., long contact histories, high-volume CRM data). • Create tooling that allows other engineering teams to ship assistants/agents faster, with consistent APIs and reusable primitives. • Build and maintain evaluation and measurement approaches (offline evals, golden datasets, automated metrics, human review loops) to ensure context quality and answer accuracy. • Collaborate closely with sister platform teams and downstream product engineering teams (your “customers”) to integrate platform capabilities into real experiences. • Own end-to-end delivery: architecture, implementation, observability, performance, reliability, and iteration.

🎯 Requirements

• Strong track record shipping production backend systems as a senior engineer (ownership from design to delivery). • Professional Java experience building maintainable, testable services in production (this is core to the role). • Experience implementing ML workflows in production (e.g., retrieval/ranking pipelines, feature/data pipelines, model/embedding services, evaluation frameworks). • Comfort working with data tooling and data-intensive systems (large datasets, pipelines, and service integrations). • Experience operating software at meaningful scale (e.g., high throughput, significant data volume, performance and reliability constraints). • Strong engineering fundamentals: system design, code quality, debugging, observability, and operational excellence.