Staff Machine Learning Engineer

Das ist der Job

Have a history of developing solutions that have had an outsized impact on a large organization’s business goals.

Das brauchst du

Role Overview Qualifikationen, Fähigkeiten und alle relevanten Erfahrungen, die für diese Stelle erforderlich sind, finden Sie in der vollständigen Beschreibung unten.

Darum lohnt es sich

The Predictive Models team is part of the AI Foundations Group at HubSpot and partners closely with the Predictive AI Platform team to deliver the next generation of predictive insights, scoring and recommendation models.

As a Staff Machine Learning Engineer on the Predictive AI Models team at HubSpot, you’ll take on many responsibilities—leveraging deep expertise in machine learning to drive product vision forward through strong collaboration and hands‑on coding.

Key Qualifications Have a long track record of delivering high‑value, high‑impact cross‑team projects; Staff MLEs are senior individual contributors expected to raise the bar for the engineering organization. Guide teams beyond the status quo; lead toward new possibilities while building a shared path forward. Embodies our engineering team values.

Wish to stay hands‑on in all technical aspects while leading by example through collaboration with cross‑functional and internal stakeholders. Provide strategic direction for major projects. Regularly mentor and teach engineers in their areas of expertise. Demonstrate pragmatic decision‑making and problem‑solving abilities.

Have expert understanding of a range of ML techniques (e.g., deep learning, optimization, regression, transformers, large language models, transfer learning, etc.) and tools (scikit‑learn, PyTorch, TensorFlow, etc.).

Expert in crafting the right architecture for a variety of ML problems from business requirements, often identifying where ML can be effective in adjacent areas. Analyze beyond offline and online metrics, considering privacy, bias, security, and maintainability of models. Show enthusiasm for building reliable, scalable systems.

Deep expertise in predictive AI concepts such as recommendation systems, classification, ranking, and relevancy.

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