Senior Machine Learning Engineer

Summary

Join HubSpot's Data, Systems & Intelligence (DSI) team as a Senior ML Engineer and contribute to building scalable AI/ML and data products for internal Sales and Customer Success teams. You will build, train, and deploy ML models and generative AI solutions, working with complex datasets and collaborating with stakeholders. This role requires expertise in machine learning techniques, Python programming, and various ML frameworks. You'll be part of a high-growth team, impacting the company's internal ML/AI maturity. The position offers opportunities for mentorship and professional growth within a supportive and collaborative environment. HubSpot values flexibility and offers both remote and in-office work options.

Requirements

• Degree in computer science, statistics, applied mathematics, economics, or other quantitative discipline
• 3+ 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
• Strong familiarity of Python programming and ML frameworks (Scikit-learn, TensorFlow, PyTorch, HuggingFace, etc.)
• 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
• Able to clearly communicate technical concepts to business leaders
• Creative, collaborative problem solver with experience delivering iterative solutions to difficult problems

Responsibilities

• Build, train, evaluate, and deploy ML models and generative AI (GAI) solutions at scale
• Work with 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

Preferred Qualifications

• MS or PhD in a quantitative field
• Familiarity with Java
• 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
• Familiarity with vector databases

Benefits

• Remote work, flexible hours
• In-person onboarding and events (with accommodation options)