HubSpot

Staff Machine Learning Engineer

HubSpot

full-time

Posted on:

Origin:  • 🇺🇸 United States

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Salary

💰 £NaN per year

Job Level

Lead

Tech Stack

GoPyTorchScikit-LearnTensorflow

About the role

  • HubSpot is an all-in-one marketing, sales, and service software platform that helps businesses grow and succeed.\n
  • The Signals team is part of the AI Platform Group at HubSpot and delivers predictive data products to product teams across the company, enabling them to create easy, accurate, and consistent AI features for our millions of customers and their customers.\n
  • As a Staff Machine Learning Engineer on the Signals team, you’ll do many things— and you likely already have experience doing some of them in the past.\n
  • We are looking for people who:\n
  • Have a long track record of experience delivering high-value, high-impact, cross-team projects.\n
  • Wish to stay hands-on in all technical aspects while leading by example through collaborations with cross-functional and internal stakeholders.\n
  • Have a history of developing solutions to problems that have had an outsized impact on a large organization’s business goals.\n
  • Provide strategic direction for major projects.\n
  • Regularly mentor and teach engineers in their areas of expertise.\n
  • Demonstrate pragmatic decision-making and problem-solving abilities.\n
  • Expert understanding of a range of ML techniques (e.g., deep learning, optimization, regression, transformers, large language models, transfer learning etc.).\n
  • 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.\n
  • Your analysis expands beyond offline and online metrics. They analyze privacy, bias, security and maintainability concerns of models developed by their team.\n
  • Exhibit an enthusiasm for building reliable, scalable systems.\n
  • 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.\n
  • Deep expertise in the machine learning concepts behind Predictive AI (such as recommendation algorithms/systems, binary and multiclass classification, ranking and relevancy).\n
  • Embodies our engineering team values.

Requirements

  • Have a long track record of experience delivering high-value, high-impact, cross-team projects.\n
  • Staff MLEs are one of the most senior individual contributors at HubSpot; they are leaders that continually strive to raise the bar for the engineering organization as a whole, and are expected to help drive the product vision forward via strong collaboration skills and hands-on coding.\n
  • Wish to stay hands-on in all technical aspects while leading by example through collaborations with cross-functional and internal stakeholders.\n
  • Have a history of developing solutions to problems that have had an outsized impact on a large organization’s business goals.\n
  • Provide strategic direction for major projects.\n
  • Regularly mentor and teach engineers in their areas of expertise.\n
  • Demonstrate pragmatic decision-making and problem-solving abilities.\n
  • Expert understanding of a range of ML techniques (e.g., deep learning, optimization, regression, transformers, large language models, transfer learning etc.).\n
  • 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.\n
  • Your analysis expands beyond offline and online metrics. They analyze privacy, bias, security and maintainability concerns of models developed by their team.\n
  • Exhibit an enthusiasm for building reliable, scalable systems.\n
  • 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.\n
  • Deep expertise in the machine learning concepts behind Predictive AI (such as recommendation algorithms/systems, binary and multiclass classification, ranking and relevancy).\n
  • Embodies our engineering team values.