Flywheel Context (Foundations) Team
UK - we're open to fully remote, hybrid or office based - whatever works best for you!
Role Summary
HubSpot is building the next generation of AI-powered experiences across our go-to-market platform. We’re hiring a Senior Software Engineer II to join the Flywheel Context team - a foundational group responsible for building the data and context layer that powers intelligent AI assistants and agents across HubSpot.
This is a backend-leaning, data-intensive role focused on building durable production systems. You’ll design and ship the infrastructure that enables AI systems to access relevant customer context, process large volumes of CRM data, and deliver high-quality outputs at scale.
If you’re excited about building foundational platforms that unlock smarter AI across an entire product ecosystem, this role is for you.
What You’ll Do
- Design, build, and operate backend services that power context retrieval, enrichment, and insight generation across HubSpot’s platform.
- Build systems for storing, processing, and retrieving high-volume GTM data (e.g., contact history, CRM activity, behavioral signals).
- Develop scalable data processing and streaming solutions to support AI-driven use cases.
- Create reusable platform capabilities and APIs that enable other product teams to build smarter AI assistants and agents.
- Contribute to evaluation and quality frameworks to ensure context accuracy, reliability, and performance.
- Collaborate closely with platform teams and downstream product engineering teams to integrate capabilities into real customer experiences.
- Own end-to-end delivery: architecture, implementation, observability, performance, and iteration in production environments.
What We’re Looking For:
- Strong track record shipping production backend systems as a senior engineer, with ownership from design through operation.
- Professional experience building maintainable, scalable backend services (Java preferred).
- Strong data background and experience working with large datasets, data pipelines, and data-intensive systems.
- Experience building or integrating AI/ML-adjacent systems in production (e.g., retrieval pipelines, embeddings, ranking systems, model-backed services, or similar).
- Experience operating systems at scale, including performance optimization and reliability considerations.
- Strong engine