Principal Machine Learning Engineer- AI Context

HubSpot

14h•$285,800 - $457,300•Hybrid

About The Position

The AI Platform Group at HubSpot delivers the ML and AI foundations that enable product teams across the company to create easy, accurate, and consistent AI features for our millions of customers and their customers. This Principal Machine Learning Engineer role will focus on AI Context: building the systems that help HubSpot's AI understand customer, company, activity, and workflow data across the CRM platform. As a Principal Machine Learning Engineer at HubSpot, you'll help define the technical direction for applied ML and AI systems that transform complex data into customer value. You will work across product, engineering, data, and ML teams to take ambiguous 0-to-1 opportunities through model development, evaluation, productionization, experimentation, and measurable customer or business impact.

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.
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.
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.
Embody our engineering team values.

Responsibilities

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.
Craft 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.
Build reliable, scalable systems for data processing, feature generation, context retrieval, model training, inference, experimentation, monitoring, and feedback loops.
Guide teams beyond the status quo; lead us beyond what we have and toward what we can build, while creating a shared notion of how to get there.
Turn 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.

Benefits

AI-powered customer platform
all-in-one marketing, sales, and service software platform
user-friendly interface and powerful tools
comprehensive solution that empowers businesses to succeed in the digital age
ML and AI foundations that enable product teams
easy, accurate, and consistent AI features
AI Context: building the systems that help HubSpot's AI understand customer, company, activity, and workflow data across the CRM platform
transform complex data into customer value
work across product, engineering, data, and ML teams
take ambiguous 0-to-1 opportunities through model development, evaluation, productionization, experimentation, and measurable customer or business impact
technical direction for applied ML and AI systems
stay hands-on in technical design, model development, production systems, and code
leading by example through collaboration with cross-functional and internal stakeholders
developing solutions to ambiguous problems that have had an outsized impact on a large organization's customer experience, product strategy, or business goals
strategic direction and architectural leadership for major ML and AI projects across multiple teams, systems, or product surfaces
mentor, coach, and teach engineers in their areas of expertise
helping senior ICs grow through complex technical projects
pragmatic decision-making and problem-solving abilities
strong judgment around when to use ML, LLMs, retrieval, rules, platform changes, or product changes
expert understanding of a range of ML techniques
deep learning, optimization, regression, transformers, large language models, transfer learning, retrieval, ranking, recommendations, classification, NLP, and personalization
tools and frameworks such as scikit-learn, PyTorch, TensorFlow, and modern model-serving and evaluation systems
expert in crafting the right architecture for a variety of ML and AI Context problems from business requirements
identifying where ML solutions can be effective in adjacent product areas
evaluating privacy, bias, security, reliability, cost, maintainability, model quality, and data governance concerns across the ML lifecycle
enthusiasm for building reliable, scalable systems for data processing, feature generation, context retrieval, model training, inference, experimentation, monitoring, and feedback loops
guide teams beyond the status quo
engineers who lead us beyond what we have and toward what we can build
creating a shared notion of how to get there
deep expertise in the machine learning concepts behind Applied and Predictive AI
recommendation algorithms and systems, binary and multiclass classification, ranking and relevance, semantic retrieval, embeddings, entity understanding, and experimentation
experience turning messy, incomplete, or heterogeneous data into useful AI context for customer-facing products
customer, company, activity, workflow, conversation, behavioral, CRM, or unstructured document data
collaborative work environment
HubSpot AI Group
flexibility and connection
regional HubSpot office for in-person onboarding
in-person events, such as your Product Group Summit and other gatherings
AI-powered customer platform
all the software, integrations, and resources customers need to connect marketing, sales, and service
connected platform enables businesses to grow faster by focusing on what matters most: customers
bold is our baseline
employees around the globe move fast, stay customer-obsessed, and win together
culture is grounded in four commitments: Solve for the Customer, Be Bold, Learn Fast, Align, Adapt & Go!, and Deliver with HEART
building a company where people can do their best work
focus on brilliant work, not badge swipes
combining clarity, ownership, and trust
create space for big thinking and meaningful progress
when our employees grow, our customers do too
award-winning culture
headquartered in Cambridge, MA, with employees and offices around the world
HubSpot may use AI to help screen or assess candidates, but all hiring decisions are always human
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