Principal Machine Learning Engineer

HubSpot is an all-in-one marketing, sales, and service software platform that helps businesses grow and succeed. The Principal Machine Learning Engineer role focuses on building systems that enable HubSpot's AI to understand data across the CRM platform and involves defining the technical direction for applied ML and AI systems.

Responsibilities:

• 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
• 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
• Can guide teams beyond the status quo; we need engineers who lead us beyond what we have and toward what we can build, while creating a shared notion of how to get there
• Bring deep expertise in the machine learning concepts behind Applied and Predictive AI, such as recommendation algorithms and systems, binary and multiclass classification, ranking and relevance, semantic retrieval, embeddings, entity understanding, and experimentation
• Have experience turning messy, incomplete, or heterogeneous data into useful AI context for customer-facing products, such as customer, company, activity, workflow, conversation, behavioral, CRM, or unstructured document data
• Embody our engineering team values

Requirements:

• Have a long track record of delivering high-value, high-impact, cross-team and cross-product projects
• 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
• 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
• Can guide teams beyond the status quo; we need engineers who lead us beyond what we have and toward what we can build, while creating a shared notion of how to get there
• Bring deep expertise in the machine learning concepts behind Applied and Predictive AI, such as recommendation algorithms and systems, binary and multiclass classification, ranking and relevance, semantic retrieval, embeddings, entity understanding, and experimentation
• Have experience turning messy, incomplete, or heterogeneous data into useful AI context for customer-facing products, such as customer, company, activity, workflow, conversation, behavioral, CRM, or unstructured document data
• Embody our engineering team values