Senior Machine Learning Engineer II

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 (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

Nice-to-Have

• 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)

What You'll Be Doing

• 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

Perks and Benefits

We know the confidence gap and impostor syndrome can get in the way of meeting spectacular candidates, so please don’t hesitate to apply — we’d love to hear from you. If you need accommodations or assistance due to a disability, please reach out to us using this form.

At HubSpot, we value both flexibility and connection, whether you’re a Remote employee or work from the Office. We want you to start your journey here by building strong connections with your team and peers.

If you are joining our Engineering team, you will be required to attend a regional HubSpot office for in-person onboarding. If you join our broader Product team, you’ll also attend other in-person events, such as your Product Group Summit and other gatherings, to continue building on those connections.

If you require an accommodation due to travel limitations or other reasons, please inform your recruiter during the hiring process. We are committed to supporting candidates who may need alternative arrangements.