Staff Machine Learning Engineer

Job not on LinkedIn

August 18

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HubSpot

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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.

1001 - 5000 employees

Founded 2006

🤝 B2B

☁️ SaaS

📋 Description

• Join HubSpot’s Signals team within the AI Platform Group to deliver predictive data products to product teams across the company. • As a Staff Machine Learning Engineer, contribute via hands-on coding and collaboration with cross-functional and internal stakeholders. • Provide strategic direction for major projects and mentor engineers in their areas of expertise. • Build reliable, scalable systems while maintaining privacy, bias, security, and maintainability considerations. • Lead by example, guiding teams beyond the status quo and shaping product vision through strong collaboration. • Bring deep expertise in ML techniques (deep learning, transformers, NLP, RL, etc.) and tools (sklearn, PyTorch, TensorFlow). • Craft architectures for ML problems aligned with business requirements and identify opportunities in adjacent surfaces. • Align with HubSpot engineering values and contribute to the culture.

🎯 Requirements

• Have a long track record of experience delivering high-value, high-impact, cross-team projects. • Wish to stay hands-on in all technical aspects while leading by example through collaborations with cross-functional and internal stakeholders. • Have a history of developing solutions to problems that have had an outsized impact on a large organization's business goals. • Provide strategic direction for major projects. • Regularly mentor and teach engineers in their areas of expertise. • Demonstrate pragmatic decision-making and problem-solving abilities. • Expert understanding of a range of ML techniques (e.g., deep learning, optimization, regression, transformers, large language models, transfer learning etc.), areas (e.g. NLP, RL, classification, recommendations, etc.), and tools (sklearn, pytorch, tensorflow, etc.). • Expert in crafting the right architecture for a variety of ML problems from business requirements often identifying where ML solution can be effective in adjacent surface areas. • Your analysis expands beyond offline and online metrics. They analyze privacy, bias, security and maintainability concerns of models developed by their team. • Exhibit an enthusiasm for building reliable, scalable systems. • Can guide teams beyond the status quo; we need engineers who lead us beyond what we have, and towards what we can build, while building a shared notion of how to get there. • Deep expertise in the machine learning concepts behind Predictive AI (such as recommendation algorithms/systems, binary and multiclass classification, ranking and relevancy) • Embodies our engineering team values.