The opportunity
HubSpot is an all-in-one marketing, sales, and service software platform that helps businesses grow and succeed. With a user-friendly interface and powerful tools, HubSpot enables businesses to attract, engage, and delight customers, ultimately driving growth and increasing revenue.
What you'll do
Have a long track record of delivering high-value, high-impact, cross-team: and cross-product projects. Principal MLEs are among the most senior individual contributors at HubSpot; they continually raise the technical bar for the engineering and ML organizations, help shape product vision, and build shared technical direction through strong collaboration and hands-on execution.
Wish to stay hands-on in technical design, model development, production: systems, and code while leading by example through collaboration with cross-functional and internal stakeholders.
Have a history of developing solutions to ambiguous problems that have had an: outsized impact on a large organization's customer experience, product strategy, or business goals.
Provide strategic direction and architectural leadership for major ML and AI: projects across multiple teams, systems, or product surfaces.
Regularly mentor, coach, and teach engineers in their areas of expertise,: including helping senior ICs grow through complex technical projects.
Demonstrate pragmatic decision-making and problem-solving abilities,: including strong judgment around when to use ML, LLMs, retrieval, rules, platform changes, or product changes.
What they're looking for
- Have expert understanding of a range of ML techniques, such as deep learning,: optimization, regression, transformers, large language models, transfer learning, retrieval, ranking, recommendations, classification, NLP, and personalization, as well as tools and frameworks such as scikit-learn, PyTorch, TensorFlow, and modern model-serving and evaluation systems.
- Are expert in crafting the right architecture for a variety of ML and AI: Context problems from business requirements, often identifying where ML solutions can be effective in adjacent product areas.
- Expand analysis beyond offline and online metrics by evaluating privacy,: bias, security, reliability, cost, maintainability, model quality, and data governance concerns across the ML lifecycle.
- Exhibit enthusiasm for building reliable, scalable systems for data: processing, feature generation, context retrieval, model training, inference, experimentation, monitoring, and feedback loops.
