Responsibilities
- Define technical direction and provide architectural leadership for major machine learning and AI projects across teams, systems, and product surfaces.
- Develop and productionize models and reliable systems for data processing, feature generation, context retrieval, model training, inference, experimentation, monitoring, and feedback loops.
- Lead ambiguous 0-to-1 opportunities from model development and evaluation through productionization and measurable customer or business impact.
- Transform complex and heterogeneous CRM, customer, company, activity, workflow, conversation, behavioral, and document data into useful AI context.
- Evaluate ML lifecycle concerns including privacy, bias, security, reliability, cost, maintainability, model quality, and data governance.
- Collaborate across product, engineering, data, ML, and other cross-functional teams to shape product vision and shared technical direction.
- Mentor, coach, and teach engineers, including helping senior individual contributors grow through complex technical projects.
- Make pragmatic decisions about when to use ML, large language models, retrieval, rules, platform changes, or product changes.
Requirements
- A long track record of delivering high-value, high-impact, cross-team and cross-product projects in a large organization.
- Principal-level individual contributor experience with hands-on technical design, model development, production systems, and coding.
- Expert understanding of machine learning techniques including deep learning, optimization, regression, transformers, large language models, transfer learning, retrieval, ranking, recommendations, classification, NLP, and personalization.
- Expertise in applied and predictive AI concepts including recommendation systems, binary and multiclass classification, ranking and relevance, semantic retrieval, embeddings, entity understanding, and experimentation.
- Experience designing architectures for ML and AI Context problems based on business requirements and identifying effective ML opportunities in adjacent product areas.
- Experience building reliable and scalable ML systems for data processing, feature generation, training, inference, experimentation, monitoring, and feedback loops.
- Experience turning messy, incomplete, or heterogeneous data into AI context for customer-facing products.
- Experience with scikit-learn, PyTorch, TensorFlow, modern model-serving systems, and model-evaluation systems.
- Strong strategic judgment, pragmatic problem-solving ability, collaboration skills, and a history of outsized impact on customer experience, product strategy, or business goals.
Benefits
- HubSpot supports remote and office-based work arrangements, with regional office attendance required for in-person engineering onboarding.
- Engineering and broader Product employees attend in-person connection events such as Product Group Summits and other gatherings.
- Accommodations and alternative arrangements are available for candidates with travel limitations or other needs.
Tech Stack
Categories
About HubSpot
HubSpot is a leading agentic customer platform that provides software and support to help businesses grow better. Our platform includes marketing, sales, service, and website management products that start free and scale to meet our customers’ needs at any stage of growth. Today, thousands of customers around the world use our powerful and easy-to-use tools and integrations to attract, engage, and delight customers. HubSpot's award-winning culture has been recognized by Glassdoor, Great Place to Work, Comparably, Fortune, Entrepreneur, Inc., and more. We build connections, careers, and employee growth by creating a workplace that values flexibility, autonomy, and transparency. You can learn more about our commitment to creating an inclusive and diverse workplace in the HubSpot Culture Code. HubSpot is a flexible company with employees working fully remotely, from an office, or a mix of the two. We are headquartered in Cambridge, MA with offices in Amsterdam, Berlin, Bogota, Dublin, Ghent, London, Madrid, Paris, San Francisco, Singapore, Sydney, Tokyo and Toronto. To learn more about HubSpot, visit www.hubspot.com and to join our team, visit www.hubspot.com/careers