Hubspot is hiring: Senior Machine Learning Engineer in Cambridge
Hubspot, Cambridge, MA, United States
Senior Machine Learning Engineer
Operations
Remote - Ireland
POS-P660
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.
We know the confidence gap and impostor syndrome can get in the way of meeting spectacular candidates, so please don’t hesitate to apply — we’d love to hear from you.
If you need accommodations or assistance due to a disability, please reach out to us using this form.
Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Germany Applicants: (m/f/d) - link to HubSpot's Career Diversity page here.
India Applicants: link to HubSpot India's equal opportunity policy here.
HubSpot, Inc. is an equal opportunity employer. As a federal contractor, we take affirmative action to ensure equal opportunity and all candidates are considered without regard to race, color, religion, national origin, age, sex, sexual orientation, gender identity, marital status, ancestry, physical or mental disability, veteran status, or any other legally protected characteristics.
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Operations
Remote - Ireland
POS-P660
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.
We know the confidence gap and impostor syndrome can get in the way of meeting spectacular candidates, so please don’t hesitate to apply — we’d love to hear from you.
If you need accommodations or assistance due to a disability, please reach out to us using this form.
Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Germany Applicants: (m/f/d) - link to HubSpot's Career Diversity page here.
India Applicants: link to HubSpot India's equal opportunity policy here.
HubSpot, Inc. is an equal opportunity employer. As a federal contractor, we take affirmative action to ensure equal opportunity and all candidates are considered without regard to race, color, religion, national origin, age, sex, sexual orientation, gender identity, marital status, ancestry, physical or mental disability, veteran status, or any other legally protected characteristics.
#J-18808-Ljbffr