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About This Role
Join HubSpot's Design Team
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The Agentic Go\-To\-Market (GTM) team is focused on helping HubSpot grow faster by using AI to dramatically improve how our own frontline Sales and Customer Success (CS) reps work. We're building an agentic\-first system that anticipates rep needs, automates the repetitive, and surfaces the right context at every step of the customer conversation — so reps can focus on what matters most: having the best conversations possible with our customers, helping them reach their goals and get the most out of HubSpot.
As a designer on the Meeting Execution team, you'll own the full arc of the meeting execution experience for both our Sales and CS reps — from preparing for a call, running it, demoing our product, all the way through to following up with next steps and outcomes. This is one of the most important areas where we can improve efficiency and efficacy for our frontline. And you'll be doing it working at the very edge of what's possible in applied AI, defining what's next for a $30B\+ company.
Why this team?
Own end\-to\-end. You'll own the full meeting execution flow across multiple rep types — Sales and CS — giving you broad scope within a focused problem space.
Work at the frontier of applied AI. We're building agentic\-first experiences — using agents to prep calls, surface the right information at the right moment, and make the full workflow as effortless as possible for our reps. You'll be shaping what that looks like from the ground up.
Design for users you actually work with. Your users are HubSpot's own Sales and Customer Success reps — your colleagues. That means faster, more direct access to research, feedback, and real\-world impact than most product roles can offer.
Drive metrics that matter. Connected call rates, conversation quality, and deal progression — your work has a direct line to the numbers that drive how HubSpot runs as a business.
Get real strategic depth. Because we design for our own company, you'll have direct visibility into HubSpot's business strategy, our sales motion, and our customer success approach — and you get to build that context directly into what you ship. That's something most product designers never get.
What You'll Do
- Own the end\-to\-end design of the meeting execution experience for HubSpot's Sales and Customer Success reps
- Work closely with reps, managers, and cross\-functional partners to deeply understand workflows, pain points, and opportunities
- Partner directly with Sales and Customer Success business leadership to translate business strategy and GTM priorities into product experience — you'll need to be as comfortable in a room with business stakeholders as you are with your product triad
- Design agentic\-first experiences that reduce friction and help reps show up to every conversation ready
- Contribute to a shared design language and patterns that scale across rep types and workflows
- Translate complex workflows into clear, intuitive, and high\-quality product experiences
- Partner with your Product Manager and Engineering triad to move from insight to shipped product
What You'll Bring
These are the foundational skills and experience we're looking for — the baseline for being effective in this role:
- Mid to senior product design experience, with a track record of owning complex workflows end\-to\-end
- Comfort with ambiguity and the ability to drive clarity through strong craft and structured thinking
- Experience designing for enterprise workflows or internal tooling, or a strong desire to go deep in that space
- Curiosity about AI and agentic systems — you don't need to have built them before, but you should be excited to
- Strong collaboration and communication skills, with experience working across a cross\-functional product team
- A user\-first instinct, with a track record of independently running research to ground your design decisions
To succeed, you'll need:
Beyond the baseline, these are the qualities that will set you apart and help you thrive on this specific team:
- A growth mindset — curious, experimental, and not tied to a single way of working
- Strong concept visualization and prototyping skills — from decks to higher\-fidelity prototypes; experience with AI\-assisted prototyping tools is a plus
- Comfort operating across multiple stakeholders, incentives, and nonlinear problems
- Excellent communication skills and the ability to tell clear, compelling stories that build alignment across teams
- Comfort engaging with both business and product stakeholders — you'll work across Sales and Customer Success leadership as well as your product triad, and should be able to hold your own in both rooms
- Systems thinking at a workflow level — the ability to hold a complex, multi\-touchpoint experience in your head while designing individual moments with care
- Comfort with AI\-native design — not bolting AI onto existing flows, but thinking from the ground up about what agentic\-first experiences look and feel like
- Sharp research instincts — with direct access to your users every day, a strong candidate uses that access proactively, not just when asked
- A high craft bar — our reps are discerning users; polish and usability matter as much as strategic thinking
*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**.*
*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*
*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**.*
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*
*HubSpot may use AI to help screen or assess candidates, but all hiring decisions are always human. More information can be found* *here**. By submitting your application, you agree that HubSpot may collect your personal data for recruiting, global organization planning, and related purposes. We may use CLEAR ID Verification during the hiring process to confirm your identity and help maintain a safe, secure, and trusted experience for all candidates. Refer to HubSpot's* *Recruiting Privacy Notice* *for details on data processing and your rights.*
Salary Context
This $120K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $120K to $180K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
HubSpot AI Hiring
HubSpot has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Cambridge, MA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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