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About This Role
POS-33520
Principal Machine Learning Engineer
HubSpot is an all-in-one marketing, sales, and service software platform that helps businesses grow and succeed. With a user-friendly interface and powerful tools, HubSpot enables businesses to attract, engage, and delight customers, ultimately driving growth and increasing revenue. From marketing automation to CRM, HubSpot offers a comprehensive solution that empowers businesses to succeed in the digital age.
About the Team
HubSpot is building the foundational AI infrastructure that powers our next generation of agentic product experiences. We're looking for a Principal Machine Learning Engineer to help shape the base layer that product teams build on: agent runtime, evaluation systems, quality signals, model optimization, fine-tuning, and model routing.
This team owns Breeze Engine, the platform behind Breeze Assistant and other AI-powered product experiences, as well as Aviator, HubSpot's internal framework for building and running agents. This is not a feature-team role it is a high-scope infrastructure role focused on building systems that create leverage across the company.
What You'll Work On
- Build infrastructure for agent runtime, evaluation, quality measurement, and model optimization.
- Create tooling that helps teams understand where their evals are strong, where coverage is missing, and where users are asking questions the system is not yet prepared to handle.
- Design signal pipelines that surface user frustration, agent failure states, and quality issues before they show up as CSAT drops.
- Help define a HubSpot-specific AI benchmark for evaluating model performance on real HubSpot workloads.
- Build systems to evaluate new models across agents, improving product quality, reducing cost, and supporting future model routing.
- Make fine-tuning and task-specific optimization more repeatable for HubSpot's AI use cases.
What We're Looking For
We're looking for a senior technical leader who:
- Has deep experience with production ML, LLM, or AI infrastructure.
- Has built systems at scale where reliability, latency, quality, and cost matter.
- Thinks in platforms, not one-off solutions.
- Can work across infrastructure, product, backend, and ML teams.
- Has experience with areas like eval frameworks, model serving, fine-tuning, signal extraction, experimentation, feedback loops, or model optimization.
- Is excited by ambiguous, foundational problems where many of the answers still need to be invented.
- Can influence senior engineers and leaders through technical credibility and clear judgment.
Why This Role Matters
This is a chance to help define how AI infrastructure works across HubSpot. The person in this role will help set the technical direction for how we evaluate, improve, and scale AI agents across the company.
At HubSpot, we value both flexibility and connection. Whether you're a Remote employee or work from the Office, we want you to start your journey here by building strong connections with your team and peers. If you are joining our Engineering team, you will be required to attend a regional HubSpot office for in-person onboarding. If you join our broader Product team, you'll also attend other in-person events, such as your Product Group Summit and other gatherings, to continue building on those connections.
If you require an accommodation due to travel limitations or other reasons, please inform your recruiter during the hiring process. We are committed to supporting candidates who may need alternative arrangements
About HubSpot
HubSpot (NYSE: HUBS) is an AI-powered customer platform with all the software, integrations, and resources customers need to connect marketing, sales, and service. HubSpot's connected platform enables businesses to grow faster by focusing on what matters most: customers.
At HubSpot, bold is our baseline. Our employees around the globe move fast, stay customer-obsessed, and win together. Our culture is grounded in four commitments: Solve for the Customer, Be Bold, Learn Fast, Align, Adapt & Go!, and Deliver with HEART. These commitments shape how we work, lead, and grow.
We're building a company where people can do their best work. We focus on brilliant work, not badge swipes. By combining clarity, ownership, and trust, we create space for big thinking and meaningful progress. And we know that when our employees grow, our customers do too.
Recognized globally for our award-winning culture by Comparably, Glassdoor, Fortune, and more, HubSpot is headquartered in Cambridge, MA, with employees and offices around the world.
Explore more:
- HubSpot Careers
- Life at HubSpot on Instagram
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Salary Context
This $285K-$457K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At HubSpot, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($371K) sits 75% above the category median. Disclosed range: $285K to $457K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
HubSpot AI Hiring
HubSpot has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $457K - $457K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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