Senior Machine Learning Engineer
POS-29735
HubSpot's mission is to Help Millions of Companies Grow Better, and we believe recent advances in AI/ML will fundamentally transform how our Marketing organization identifies, engages, and converts prospects. We're seeking a talented Senior Machine Learning Engineer to join our Marketing Data Science team, where you'll be at the forefront of building and optimizing AI agents, LLM-powered solutions, and production ML systems that drive our marketing intelligence capabilities.
Our team currently operates a sophisticated ecosystem of AI-driven and real-time ML models in production, including prospect intent scoring, sentiment analysis systems, as well as lead qualification and routing models. We need a senior engineer who can elevate our engineering practices, ensure model reliability at scale, and help us expand our AI capabilities to support increasingly complex marketing automation and personalization use cases.
You'll be joining a dynamic Marketing Data Science team that deeply values technical excellence, innovation, autonomy, and measurable business impact. Our models and AI systems directly influence how HubSpot markets to millions of prospects, enabling hyper-personalized experiences and data-driven decision making across all marketing channels. With HubSpot's marketing AI capabilities rapidly expanding in scope and importance, this is a unique opportunity to shape the future of AI-powered loop marketing at scale.
You'll collaborate closely with Data Scientists developing new models, the team managing our deployment infrastructure, reporting teams consuming our predictions, and cross-functional teams building AI-powered marketing tools. Your work will directly impact our ability to understand prospect intent, optimize marketing spend, and deliver the right message to the right prospect at the right time.
Objectives of this Role
Lead AI Agent Development - Design and implement LLM-powered agents for marketing automation, feature extraction, unstructured data analysis, campaign management and conversational marketing applications using various language models.
Optimize Production ML Systems - Take ownership of our existing real-time scoring pipelines (including chatbot interaction scores, sentiment models, and intent classifiers), driving improvements in performance, reliability, and cost efficiency.
Build Scalable ML Infrastructure - Develop robust pipelines for model training, evaluation, and deployment supporting both batch and real-time inference at scale (processing millions of predictions daily).
Champion ML Best Practices - Establish and enforce engineering standards for model versioning, A/B testing, monitoring, model auto-deployment and retraining across our diverse portfolio of production models.
Process Complex Marketing Data - Enable the querying, integration and preprocessing of complex unstructured datasets including emails, webchats, calls and application usage behaviours to extract context and usable predictive features.
Drive End-to-End ML Projects: Help develop projects from initial stakeholder requirements through production deployment and ongoing optimization, with strong focus on marketing ROI and attribution.
Enable Marketing Intelligence: Partner with marketing stakeholders to translate business problems into AI/ML solutions, focusing on prospect scoring, attribution modeling, and campaign optimization through interactive AI-driven frameworks.
Required Qualifications
3+ years of ML engineering experience with multiple ML models deployed in production settings, preferably in marketing or customer analytics domains. A Masters or PhD in a relevant field is a plus.
Deep expertise in Python ML frameworks including Scikit-learn, H2O, TensorFlow, PyTorch, with experience integrating ChatGPT, LangChain or similar for LLM applications.
Production ML deployment experience with Docker, Kubernetes, MLFlow, and model serving infrastructure.
Strong data engineering skills with Snowflake SQL, Python, dbt, FiveTran, Workato, Looker as well as experience processing structured and unstructured data at scale.
LLM and GenAI implementation experience including prompt engineering, fine-tuning, RAG architectures, and production deployment of language models.
Proven track record of improving existing ML systems, for example by optimizing inference latency, reducing costs, improving model accuracy, and ensuring system reliability.
Experience with real-time ML systems including streaming data processing, online feature stores, and low-latency prediction serving.
Strong communication skills to articulate technical concepts to marketing stakeholders and document complex ML systems.
Bonus Points
Java programming skills for model scoring integration and legacy system support.
Marketing domain expertise including familiarity with lead scoring, attribution modeling, customer segmentation, and marketing automation platforms.
Experience with streaming architectures using Kafka, SQS, Kinesis, or similar for real-time feature engineering.
Advanced NLP experience including entity extraction, topic modelling, intent classification, and sentiment analysis from customer communications.
Prior B2B SaaS experience especially supporting marketing or growth teams with data science solutions.
Expertise in experimentation using A/B testing, including multi-armed bandits, contextual bandits, and causal inference methods.
Experience with ML monitoring tools like MonteCarlo, DataDog, PagerDuty or custom alerting systems for model drift detection.
Why Join Our Team?
This is an exceptional opportunity to join a team that's pushing the boundaries of AI in marketing. You'll work on cutting-edge problems at the intersection of machine learning, marketing science, and business strategy. Your contributions will directly impact how HubSpot identifies and engages with millions of prospects globally, and you'll have the autonomy to drive significant technical and business outcomes.
We offer the unique combination of startup-like innovation with the resources and scale of an established platform, giving you the best of both worlds for making a meaningful impact with AI/ML in marketing.
Senior Machine Learning Engineer
POS-29735
HubSpot's mission is to Help Millions of Companies Grow Better, and we believe recent advances in AI/ML will fundamentally transform how our Marketing organization identifies, engages, and converts prospects. We're seeking a talented Senior Machine Learning Engineer to join our Marketing Data Science team, where you'll be at the forefront of building and optimizing AI agents, LLM-powered solutions, and production ML systems that drive our marketing intelligence capabilities.
Our team currently operates a sophisticated ecosystem of AI-driven and real-time ML models in production, including prospect intent scoring, sentiment analysis systems, as well as lead qualification and routing models. We need a senior engineer who can elevate our engineering practices, ensure model reliability at scale, and help us expand our AI capabilities to support increasingly complex marketing automation and personalization use cases.
You'll be joining a dynamic Marketing Data Science team that deeply values technical excellence, innovation, autonomy, and measurable business impact. Our models and AI systems directly influence how HubSpot markets to millions of prospects, enabling hyper-personalized experiences and data-driven decision making across all marketing channels. With HubSpot's marketing AI capabilities rapidly expanding in scope and importance, this is a unique opportunity to shape the future of AI-powered loop marketing at scale.
You'll collaborate closely with Data Scientists developing new models, the team managing our deployment infrastructure, reporting teams consuming our predictions, and cross-functional teams building AI-powered marketing tools. Your work will directly impact our ability to understand prospect intent, optimize marketing spend, and deliver the right message to the right prospect at the right time.
Objectives of this Role
Lead AI Agent Development - Design and implement LLM-powered agents for marketing automation, feature extraction, unstructured data analysis, campaign management and conversational marketing applications using various language models.
Optimize Production ML Systems - Take ownership of our existing real-time scoring pipelines (including chatbot interaction scores, sentiment models, and intent classifiers), driving improvements in performance, reliability, and cost efficiency.
Build Scalable ML Infrastructure - Develop robust pipelines for model training, evaluation, and deployment supporting both batch and real-time inference at scale (processing millions of predictions daily).
Champion ML Best Practices - Establish and enforce engineering standards for model versioning, A/B testing, monitoring, model auto-deployment and retraining across our diverse portfolio of production models.
Process Complex Marketing Data - Enable the querying, integration and preprocessing of complex unstructured datasets including emails, webchats, calls and application usage behaviours to extract context and usable predictive features.
Drive End-to-End ML Projects: Help develop projects from initial stakeholder requirements through production deployment and ongoing optimization, with strong focus on marketing ROI and attribution.
Enable Marketing Intelligence: Partner with marketing stakeholders to translate business problems into AI/ML solutions, focusing on prospect scoring, attribution modeling, and campaign optimization through interactive AI-driven frameworks.
Required Qualifications
3+ years of ML engineering experience with multiple ML models deployed in production settings, preferably in marketing or customer analytics domains. A Masters or PhD in a relevant field is a plus.
Deep expertise in Python ML frameworks including Scikit-learn, H2O, TensorFlow, PyTorch, with experience integrating ChatGPT, LangChain or similar for LLM applications.
Production ML deployment experience with Docker, Kubernetes, MLFlow, and model serving infrastructure.
Strong data engineering skills with Snowflake SQL, Python, dbt, FiveTran, Workato, Looker as well as experience processing structured and unstructured data at scale.
LLM and GenAI implementation experience including prompt engineering, fine-tuning, RAG architectures, and production deployment of language models.
Proven track record of improving existing ML systems, for example by optimizing inference latency, reducing costs, improving model accuracy, and ensuring system reliability.
Experience with real-time ML systems including streaming data processing, online feature stores, and low-latency prediction serving.
Strong communication skills to articulate technical concepts to marketing stakeholders and document complex ML systems.
Bonus Points
Java programming skills for model scoring integration and legacy system support.
Marketing domain expertise including familiarity with lead scoring, attribution modeling, customer segmentation, and marketing automation platforms.
Experience with streaming architectures using Kafka, SQS, Kinesis, or similar for real-time feature engineering.
Advanced NLP experience including entity extraction, topic modelling, intent classification, and sentiment analysis from customer communications.
Prior B2B SaaS experience especially supporting marketing or growth teams with data science solutions.
Expertise in experimentation using A/B testing, including multi-armed bandits, contextual bandits, and causal inference methods.
Experience with ML monitoring tools like MonteCarlo, DataDog, PagerDuty or custom alerting systems for model drift detection.
Why Join Our Team?
This is an exceptional opportunity to join a team that's pushing the boundaries of AI in marketing. You'll work on cutting-edge problems at the intersection of machine learning, marketing science, and business strategy. Your contributions will directly impact how HubSpot identifies and engages with millions of prospects globally, and you'll have the autonomy to drive significant technical and business outcomes.
We offer the unique combination of startup-like innovation with the resources and scale of an established platform, giving you the best of both worlds for making a meaningful impact with AI/ML in marketing.