Principal Machine Learning Engineer – AI Context

🔥 21 minutes ago

🍂 Massachusetts – Remote

🍂 Massachusetts – Remote

💵 $313k - $500k / year

⏰ Full Time

🔴 Lead

🤖 AI Engineer

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HubSpot

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2006

🤝 B2B

☁️ SaaS

📣 Marketing

B2B • SaaS • Marketing

HubSpot is an AI-powered customer platform that combines marketing, sales, and customer service software into one integrated suite. With over 238,000 customers in 135 countries, HubSpot offers tools for marketing automation, sales management, customer service, content marketing, operations, and B2B commerce. With products like Marketing Hub, Sales Hub, Service Hub, and Content Hub, HubSpot enables businesses to generate leads, close deals, and provide excellent customer support, all while using AI to enhance operations and insights. The platform is designed to unify teams and customer data, supporting both small startups and large enterprises in their growth journey.

📋 Description

• Help define the technical direction for applied ML and AI systems that transform complex data into customer value. • 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. • 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. • Are expert in crafting the right architecture for a variety of ML and AI Context problems from business requirements. • 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.