Requirements

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 engineering fundamentals: system design, testing, debugging, observability, and operational excellence.
Product mindset - comfortable collaborating cross-functionally and building platform capabilities used by other engineering teams.

Nice to Have

Experience with search, retrieval, ranking, or relevance systems.
Familiarity with RAG architectures, vector search, or hybrid retrieval approaches.
Experience with streaming technologies or event-driven architectures.
Cloud-native development experience (e.g., Kubernetes, AWS/GCP).
Some frontend experience or willingness to contribute across the stack when needed.
Python experience.

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.

Flexible working arrangements (remote, hybrid, or office-based).
Opportunity to work on foundational platforms that drive AI innovation.
Directly impact the reliability and effectiveness of AI agents.
Join a globally recognized company with a culture focused on learning and growth.