Machine Learning Engineer, Data & Systems Intelligence
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POS-22866
HubSpot’s mission is to Help Millions of Companies Grow Better, and we believe recent advances in AI/ML will allow our internal Go-to-Market (GTM) teams to more effectively serve even more companies, helping them to grow. We’re seeking a talented Senior Machine Learning (ML) Engineer to join our GTM Data and Systems team as part of a newly-formed GTM AI team supporting our internal Sales and Customer Success (CS) clients through the delivery of scalable AI/ML and other data products to improve the efficiency and efficacy of frontline Sales and Customer Success reps and solve for their pain points.
You will be joining a high-growth, high-powered GTM Data team of Analytic Engineers, Data Scientists, Data Engineers, and ML Engineers that deeply values intellectual curiosity, collaboration, and autonomy. The algorithms, insights, and data products we develop allow our Sales and CS reps to more effectively support our prospects and customers. It’s an exciting opportunity to make an enormous impact in a rapidly growing space–we’ve got big plans and want talented, passionate engineers to help us achieve them! (HubSpot is early in its GTM AI maturity curve, which provides a unique opportunity for enormous impact.)
You will work collaboratively not only with other ML Engineers on the team, but also Data Scientists, the ML Ops team (who provide model deployment, monitoring, and orchestration support), the GTM Data Platform team (who provide analytic feature stores and access to new data sources), our Flywheel Product team (who provide the front-end experiences reps interact with on a daily basis), and many other teams.
Objectives of this Role
Build, train, evaluate, and deploy ML models and generative AI (GAI) solutions at scale, both batch and near real time
Query, integrate, analyze, and preprocess rich and 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
Participate in code reviews, extensive testing, and documentation, ensuring continued/improved quality and maintainability of the codebase
Drive projects from stakeholder requirements gathering to deployment / monitoring with a strong focus on ROI / cross-functional collaboration
Integrate internally trained LLMs into workflows for specific use cases
Build relationships with internal stakeholders and develop an understanding of their business problems
About you:
3+ years experience in machine learning with multiple models deployed in operational settings
Knowledge of a breadth of machine learning/AI techniques and an understanding of the best approach to use for a given situation
Substantial knowledge of at least one Python programming and ML framework (Scikit-learn, h2o.ai, TensorFlow, PyTorch, HuggingFace, LangChain, etc.)
Familiarity with Snowflake (or similar cloud warehouse), SQL, as well as dbt and jinja templating
Familiarity with CI/CD systems (e.g. GitHub Actions, Jenkins, etc.)
Familiarity with monitoring & alerting systems (Monte Carlo, Cloudwatch, DataDog, GreatExpectations. etc.)
Familiarity with standard ML deployment stack (Docker, Kubernetes, MLflow, wandb, etc.)
Ability to own minimally scoped pieces of an ML software project at any stage of the SDLC with guidance from more senior engineers
Track record of delivering successful ML/AI products
Able to clearly communicate highly technical concepts to business leaders in both slides and memos
Creative, collaborative problem solver with experience delivering iterative solutions to difficult problems
Bonus points:
Java programming skills
Experience working with kafka or other streaming data formats
Prior industrial experience with RAG, LLMs, NLP
Prior experience supporting GTM teams or functions, especially in B2B SaaS companies
Experience with design of experiments (DoE) beyond simple A/B testing
About the job
Machine Learning Engineer, Data & Systems Intelligence
To see similar active jobs please follow this link: Remote Development jobs
POS-22866
HubSpot’s mission is to Help Millions of Companies Grow Better, and we believe recent advances in AI/ML will allow our internal Go-to-Market (GTM) teams to more effectively serve even more companies, helping them to grow. We’re seeking a talented Senior Machine Learning (ML) Engineer to join our GTM Data and Systems team as part of a newly-formed GTM AI team supporting our internal Sales and Customer Success (CS) clients through the delivery of scalable AI/ML and other data products to improve the efficiency and efficacy of frontline Sales and Customer Success reps and solve for their pain points.
You will be joining a high-growth, high-powered GTM Data team of Analytic Engineers, Data Scientists, Data Engineers, and ML Engineers that deeply values intellectual curiosity, collaboration, and autonomy. The algorithms, insights, and data products we develop allow our Sales and CS reps to more effectively support our prospects and customers. It’s an exciting opportunity to make an enormous impact in a rapidly growing space–we’ve got big plans and want talented, passionate engineers to help us achieve them! (HubSpot is early in its GTM AI maturity curve, which provides a unique opportunity for enormous impact.)
You will work collaboratively not only with other ML Engineers on the team, but also Data Scientists, the ML Ops team (who provide model deployment, monitoring, and orchestration support), the GTM Data Platform team (who provide analytic feature stores and access to new data sources), our Flywheel Product team (who provide the front-end experiences reps interact with on a daily basis), and many other teams.
Objectives of this Role
Build, train, evaluate, and deploy ML models and generative AI (GAI) solutions at scale, both batch and near real time
Query, integrate, analyze, and preprocess rich and 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
Participate in code reviews, extensive testing, and documentation, ensuring continued/improved quality and maintainability of the codebase
Drive projects from stakeholder requirements gathering to deployment / monitoring with a strong focus on ROI / cross-functional collaboration
Integrate internally trained LLMs into workflows for specific use cases
Build relationships with internal stakeholders and develop an understanding of their business problems
About you:
3+ years experience in machine learning with multiple models deployed in operational settings
Knowledge of a breadth of machine learning/AI techniques and an understanding of the best approach to use for a given situation
Substantial knowledge of at least one Python programming and ML framework (Scikit-learn, h2o.ai, TensorFlow, PyTorch, HuggingFace, LangChain, etc.)
Familiarity with Snowflake (or similar cloud warehouse), SQL, as well as dbt and jinja templating
Familiarity with CI/CD systems (e.g. GitHub Actions, Jenkins, etc.)
Familiarity with monitoring & alerting systems (Monte Carlo, Cloudwatch, DataDog, GreatExpectations. etc.)
Familiarity with standard ML deployment stack (Docker, Kubernetes, MLflow, wandb, etc.)
Ability to own minimally scoped pieces of an ML software project at any stage of the SDLC with guidance from more senior engineers
Track record of delivering successful ML/AI products
Able to clearly communicate highly technical concepts to business leaders in both slides and memos
Creative, collaborative problem solver with experience delivering iterative solutions to difficult problems
Bonus points:
Java programming skills
Experience working with kafka or other streaming data formats
Prior industrial experience with RAG, LLMs, NLP
Prior experience supporting GTM teams or functions, especially in B2B SaaS companies
Experience with design of experiments (DoE) beyond simple A/B testing