Senior Machine Learning Engineer II

POS-31662

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Role Summary

HubSpot is building the next generation of AI experiences across our go-to-market products. We’re hiring a Senior Machine Learning Engineer II to join the Flywheel Context (Contacts) team, where you’ll build the context platform layer that powers accurate, high-performing AI assistants and agents.

This is a backend-leaning ML engineering role focused on shipping production software—designing systems that help other engineering teams retrieve relevant facts, manage long/complex context, and evaluate quality at scale. If you love building durable platforms (not just prototypes), this role is for you.

 

 

What You’ll Do

• 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.

 

Required Qualifications

• 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<