Staff Machine Learning Engineer

POS-P159

HubSpot’s mission is to help millions of companies Grow Better, and we believe recent advances in AI/ML will allow our internal Go-to-Market (GTM) teams to more effectively serve even more companies. We’re seeking a talented, experienced Staff Machine Learning (ML) Engineer to join our Data, Systems & Intelligence (DSI) team as part of a newly-formed GTM AI team supporting internal Sales and Customer Success (CS) clients through the delivery of scalable AI/ML and other data products to improve the efficiency and efficacy of frontline Sales and Customer Success reps and solve for their pain points. 

You will be joining a high-growth, high-powered GTM Data team of Analytic Engineers, Data Scientists, and ML Engineers that deeply values intellectual curiosity, collaboration, and autonomy. The algorithms, insights, and data products we develop allow our Sales and CS reps to more effectively support our prospects and customers. It’s an exciting opportunity to make an enormous impact in a rapidly growing space–we’ve got big plans and want talented, passionate engineers to help us achieve them! (HubSpot is early in its GTM AI maturity curve, which provides a unique opportunity for enormous impact.)

You will work collaboratively not only with other ML Engineers on the team, but also the ML Ops team (who provide model deployment, monitoring, and orchestration support), the GTM Data Platform team (who provide analytic feature stores and access to new data sources), our Flywheel Product team (who provide the front-end experiences reps interact with on a daily basis), and many other teams.

Objectives of this Role

• Build, train, evaluate, and deploy ML models and generative AI (GAI) solutions at scale, both batch and near real time

• Query, integrate, analyze, and preprocess rich and complex datasets (both structured and unstructured) to extract relevant features and insights

• Conduct experiments and evaluations of ML and generative AI models, using statistical methods and visualization tools to assess performance and identify areas for improvement

• Train and fine-tune LLMs for specific, tailored use cases

• Build strong relationships with internal stakeholders and develop a deep understanding of their business problems

• Keep current with the research and trends in AI/ML/GAI, and contribute to the development of new algorithms and techniques

• Participate in code reviews, testing, and documentation activities, ensuring high quality and maintainability of the codebase

• Mentor other junior ML Engineers and Data Scientists to improve their coding proficiency, algorithmic efficiency and general knowledge of the rapidly evolving field

About you:

• Degree in computer science, statistics, applied mathematics, economics, or other quantitative discipline

• 5+ years experience in machine learning with multiple models deployed in operational settings

• Expert knowledge of a breadth of machine learning/AI techniques and a thorough understanding of the best approach to use for a given situation

• Expert knowledge of Python programming and ML frameworks (Scikit-learn, TensorFlow, PyTorch, HuggingFace, etc.)

• Extensive familiarity with CI/CD systems (e.g. GitHub Actions, Jenkins, CircleCI, etc.)

• Familiarity with monitoring & alerting systems (DataDog, Monte Carlo, Cloudwatch)

• Familiarity with Snowflake, SQL, as well as DBT and jinja templating

• Familiarity with standard ML deployment stack (Docker, Kubernetes, Spark, dask, etc.)

• Ability to own a software project from planning to maintenance. Agile or scrum familiarity preferred. Works well with backend/frontend/full stack engineers.

• Proven track record of delivering high-impact ML/AI products

• Able to clearly communicate highly technical concepts to business leaders in both slides and memos

• Creative, collaborative problem solver with experience delivering iterative solutions to difficult problems

Bonus points:

• MS or PhD in quantitative field

• Solid java programming skills

• Experience working with kafka or other streaming data

• Prior academic or industrial experience with LLMs or RAG flows

• Prior experience supporting GTM teams or functions, especially in B2B SaaS companies

• Experience deploying enterprise-grade models in AWS

• Familiarity with vector databases

• Understanding of imposter syndrome and its extreme prevalence