Machine Learning Engineer

Role Summary

Our mission at HubSpot is to help millions of organizations grow better. We’re looking to hire a Machine Learning Engineer to join our Data & Systems Intelligence (DSI) team. On the DSI team you’ll build machine learning systems that directly support how HubSpot goes to market. As a Machine Learning Engineer, you’ll partner closely with Sales and Customer Success leaders, Data Scientists, and the wider Operations org to turn complex data into scalable, production-ready ML solutions. Your work will influence forecasting, prioritization, and strategic decision-making across GTM teams, with a strong focus on real-world impact and reliability.

What You’ll Do

• Design, build, and deploy ML- and LLM-powered systems, including predictive models, retrieval-augmented generation (RAG) pipelines, and agentic workflows that support GTM decision-making and execution.
• Work closely with Sales and Customer Success leaders to translate business questions into ML/AI-powered solutions that drive measurable outcomes.
• Apply LLM evaluation techniques (offline evals, golden datasets, human review, and automated metrics) to ensure quality, safety, and business relevance.
• Build and maintain LLM infrastructure, including vector stores, embedding pipelines, inference services, and evaluation tooling.
• Partner day-to-day with Data Scientists to productionize models, experiments, and analyses into robust, maintainable systems.
• Own the end-to-end lifecycle for both classical ML and LLM-based systems, including prompt management, retrieval strategies, tool orchestration, deployment, monitoring, and iteration.
• Build and maintain ML pipelines, LLM infrastructure, and tooling that prioritize reliability, performance, and ease of iteration.
• Apply techniques such as supervised learning, time-series forecasting, and experimentation to high-impact GTM and operations use cases.
• Monitor ML and LLM systems in production, identifying performance drift, bias, or degradation and working with Data Scientists to address issues.
• Champion strong MLOps, LLMOps, and AgentOps practices, including reproducibility, observability, documentation, and responsible model usage.
• Contribute to shared technical standards and best practices across DSI, Analytics, and GTM-facing data teams.

Required Qualifications

• Professional experience building and deploying machine learning models in production environments.
• Strong software engineering skills, with proficiency in Python and experience writing clean, testable, maintainable code.
• Experience working with large datasets and data pipelines using SQL and modern data platforms.
• Hands-on experience with ML frameworks and libraries (e.g., PyTorch, TensorFlow, scikit-learn)
• Experience collaborating closely with Data Scientists to operationalize models and experiments.
• Ability to partner with non-technical stakeholders, including Sales and Customer Success leaders, to deliver actionable solutions.
• Experience deploying or supporting classic ML and LLM / generative AI systems in production, including RAG architectures, prompt engineering, LLM evaluation frameworks, and inference optimization.
• Experience building or operating agentic systems that combine LLMs with tools, APIs, workflows, or decision logic.
• Experience deploying or supporting LLMs / generative AI systems in production, including RAG, LLM Eval frameworks, etc
• Operational fluency in Java

Nice-to-Have Qualifications

• Experience supporting go-to-market, revenue, or customer-focused teams with data or ML solutions.
• Exposure to time-series forecasting, optimization, or causal inference.
• Experience with cloud platforms and ML infrastructure (e.g., AWS, GCP, Kubernetes).
• Familiarity with responsible AI practices, including bias detection and governance.
• Familiarity with responsible generative AI practices, including prompt safety, hallucination mitigation, and human-in-the-loop review.