HubSpot is looking for a Machine Learning Engineer to build intelligent systems for local search, recommendations, and marketplace matching. You will work on search, ranking, retrieval, user-intent understanding, and personalization, using techniques like embeddings, NLP, and LLMs. The role spans from experimentation to production deployment and welcomes candidates across experience levels.
Responsibilities
Build and improve ML models for search, recommendation, ranking, retrieval, matching, and personalization.
Apply embeddings, NLP, LLMs, and modern retrieval techniques to connect consumer demand with relevant local content.
Build and optimize end-to-end ML pipelines from data preparation and model training to online serving and monitoring.
Partner with product, engineering, and data teams to translate business needs into scalable ML solutions.
Design and analyze online and offline experiments to measure model quality and product impact.
Requirements
Experience building ML, data mining, search, recommendation, ranking, NLP, or related systems.
Strong programming skills in Python, Java, C++, or another relevant language.
Solid understanding of ML fundamentals, data structures, algorithms, and statistical analysis.
Experience with search/retrieval/learning-to-rank, recommendation/personalization, query understanding/NLP, embeddings/semantic search/LLMs, or marketplace optimization.
HubSpot is a leading agentic customer platform that provides software, support, and services to help businesses grow better. The company offers a suite of integrated products including marketing, sales, customer service, content management, and data operations software.
Ability to work with large-scale datasets and translate ambiguous problems into practical solutions.
Strong collaboration and communication skills.
Nice to Have
Experience deploying and maintaining machine learning models in production.
Experience with experimentation, including A/B testing, causal analysis, or marketplace experiments.
Familiarity with deep learning frameworks and ML infrastructure such as PyTorch, TensorFlow, Spark, Kubernetes, or feature and model-serving platforms.
Experience building taxonomies, user-interest representations, knowledge graphs, or behavioral models.
Experience applying LLMs to search, recommendation, classification, or information retrieval.
Background in local search, maps, commerce, marketplaces, delivery, mobility, or location-based products.