Senior Machine Learning Engineer II

Role Summary

Hub Spot 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  you love building durable platforms (not just prototypes), this role is for you.

• 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 Hub Spot’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.

• 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.

• Experience with search/retrieval/relevance/ranking systems (highly aligned to context work).
• Experience with RAG‑style systems, embeddings, vector search, or hybrid retrieval strategies.
• Familiarity with LLM evaluation patterns (golden sets, automated metrics, human review), hallucination mitigation, and quality measurement.
• Experience with distributed systems, event‑driven architectures, or stream processing.
• Cloud/platform experience (e.g., Kubernetes, AWS/GCP) and running services in production.
• Some Python experience (useful, but not the primary language).

Hub Spot (NYSE: HUBS) is an AI‑powered customer platform with all the software, integrations, and resources customers need to connect marketing, sales, and service. Hub Spot’s connected platform enables businesses to grow faster by focusing on what matters most: customers. At Hub Spot, bold is our baseline. Our employees around the globe move fast, stay customer‑obsessed, and win together. Our culture is grounded in four commitments:
Solve for the Customer, Be Bold, Learn Fast, Align, Adapt & Go!, and Deliver with HEART. These commitments shape how we work, lead, and grow. We’re building a company where people can do their best work. We focus on brilliant work, not badge swipes. By combining clarity, ownership, and trust, we create space for big thinking and meaningful progress. And we know that when our employees grow, our customers do too.

Recognized globally for our award‑winning culture by Comparably, Glassdoor, Fortune, and more, Hub Spot is headquartered in Cambridge, MA, with employees and offices around the world.

• Hub Spot Careers
• Life at Hub Spot on Instagram

Hub Spot may use AI to help screen or assess candidates, but all hiring decisions are always human. More information can be found…