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About This Role
Location: NYC (Hybrid, 3x/week)
Compensation: $180k–$200k base salary range based on level and experience, plus a generous pre\-IPO equity package
About Hearth
Hearth is the AI growth system for local home improvement small businesses. We build revenue\-critical software for home improvement contractors — the roofers, HVAC techs, and builders who keep America running.
Our platform helps real business owners win more jobs, protect profit margins, and manage homeowner relationships professionally so they can build generational companies for their families and communities.
We are now building the next layer: a personal AI employee for every small business in the trades focused on autonomously generating sales for America’s home improvement pros. An assistant who answers every call 24/7, qualifies leads, books appointments, follows up on quotes and estimates, and helps small business owners go from missing half of their business to capturing all of it.
With 15,000\+ paying customers, default profitability, and backing from some of the world’s best investors (Founder’s Fund, 8VC, Human Capital, etc.), Hearth is focused on bringing AI to the trades.
##### The Opportunity
We are searching for the right Senior Product Designer who will help bring AI to the trades. You will own how millions of small business contractors and homeowners experience Harper \- Hearth’s AI digital employee. This is a high\-impact role that defines how a massive, underserved industry experiences AI for the first time.
You will run rapid validation and user testing with real contractors, design and ship experiences that inflect the growth rate of the business, and ultimately translate powerful technology into experiences so intuitive that a roofer in 100°F heat can use them between jobs in under five minutes.
The right person for this role is a true builder \- someone who is high agency, high urgency, and wants to have a meaningful impact. You are a true partner to engineers, accelerating time to ship and injecting the voice of the customer to unblock the team. You spend a quarter of your time with customers, and the rest of your time turning what you learn into experiments that drive commercial impact.
##### What You'll Do:
- Harper AI experience end\-to\-end. Onboarding and activation flows, the Message Center / Stream UI, intent previewing and editing, and every AI\-powered touchpoint across web, mobile, and messaging…all designed to drive real commercial outcomes in a simple, intuitive way.
- User research operations. Run weekly rapid interviews with contractors, test prototypes in the field, and synthesize qualitative signals into product decisions the entire team can act on. You’ll spend 25%\+ of your time with customers.
- AI design system and interaction patterns. Build and maintain production\-grade components for AI interfaces that reduce engineering ambiguity and accelerate the team.
- Cross\-functional translation. Turn product outcomes into acceptance criteria, engineering tickets with required instrumentation events. You’re the connective tissue between product, engineering, and the customer.
- Experimentation engine. Draft hypotheses, define success metrics, ship lightweight A/B and feature experiments, analyze impact, and iterate in days, not months. You own the full cycle from insight to shipped experiment to measured result.
##### Your First 90 Days:
- Weeks 1–2: Audit the Harper UX end\-to\-end — onboarding funnel, Message Center, product demos. Deliver a prioritized list of 3 experiments with hypotheses, target metrics, and required engineering effort.
- Month 1: Ship your first high\-impact A/B or feature experiment with a measurement plan. Get in front of customers and deliver a customer insight that directly informs the roadmap.
- Month 2: Scale experimentation cadence. Work closely with engineers and leadership to deliver on the product roadmap. Begin mapping the AI design system and deliver multiple explorations for team review.
- Month 3: Stabilize the experimentation engine, formalize the design system, and establish the research and measurement rituals that will compound from here.
##### Who You Are:
- Taste \& Impact. 3–7\+ years of product design with a strong UX practice. You’ve shipped real products, run experiments, and can point to measurable business impact from your work. You have experience successfully building for non\-technical customers.
- AI\-first. You have experience designing AI, conversational, or voice interfaces — and you’re spending your nights and weekends pushing the frontier of what’s possible with AI tools for design.
- Data fluent. You can read Amplitude dashboards, define success metrics for experiments, and use quantitative results to make design decisions. You don’t design without a hypothesis.
- Builder velocity. You move from problem to solution to shipped experiment in days, not sprints. Rapid prototyping is second nature. You produce ship\-ready components in under a day and can explain every tradeoff you made.
- Customer obsessed. You genuinely want to understand contractors — their days, their frustrations, their workflows. You’re energized by field research, not intimidated by it. You have the empathy to design for non\-technical blue\-collar users and the humility to let their reality override your assumptions.
- Systems thinker. You design consistent, scalable patterns across mobile, web, and messaging surfaces. You bridge product and engineering tradeoffs instead of creating engineering miracles.
- Communicator. You express ideas crisply. You run cross\-functional workshops and produce artifacts that engineering, sales, and leadership actually use.
##### What We’re Looking For:
- Experience designing conversational AI, voice UI, or chatbot experiences
- Background in SMB, blue\-collar, or trades\-adjacent product design
- Familiarity with mobile\-first design for field workers and on\-the\-go use cases
- History of building and scaling design systems from scratch
- Experience with Twilio, messaging platforms, or real\-time communication products
##### What We’re Not Looking For:
- You’re not AI\-first — you’re not spending every extra hour experimenting with the latest AI tools for design
- You lack “designer\-market fit” \- you are unable to spend 30% of your time with blue collar contractors and “get” them
- You design without quantification \- no hypothesis, no metric, no plan to measure impact
- You hide behind “we need more research” to avoid shipping — your research should be rapid and outcome\-oriented
- You need heavy management, detailed briefs, and structured processes to be productive
- You measure success through deliverables completed rather than customer outcomes moved
- You’re looking for a “chill” role rather than an opportunity to define how an entire industry experiences AI
##### Why This Role:
Define how an industry meets AI. You’ll be defining how millions of contractors and homeowners experience AI for the first time. Harper is early, the canvas is wide open, and the decisions you make in the first 90 days will shape the product for years. This is a rare chance to do foundational design work on a product with real customers and real revenue.
Real impact on real people. Our customers are business owners building real things. When your design works, a contractor in Phoenix closes a job he would have missed. A roofer in Texas stops losing 40% of inbound calls because Harper caught them while he was on a job site. A fence pro in Delaware gets to come home for dinner with his family instead of returning calls until 9pm. The feedback loop between your design and someone’s livelihood is short and tangible.
A real business with serious traction and massive upside. Hearth is growing, profitable, and already has 15,000\+ customers. This isn’t a proof of concept — this is an opportunity to build the most ambitious version of a product that powers a trillion\-dollar market. You would be shaping the customer experience that drives that transformation — early enough for meaningful equity and late enough for strong foundations.
##### Our Core Values
Truth. We value honesty and data. We seek to understand what is reality, so we can effectively respond to it.
Slope. Rate of change over time. We hire and reward based on a team member’s potential, capacity, and growth\-mindset, rather than a fancy resume.
Mutual Benefit. The best outcomes happen when everyone wins \- customers, team members, and the company. We seek to understand each other’s aspirations and create alignment to get there.
Competitive Greatness. We desire an opportunity and environment from which to pull the greatest versions of ourselves out into the world, rather than just a “job”.
##### Location
This role will be located in NYC along with the rest of the technical Hearth team, the CEO, and other key roles in the business. Hearth believes in the energy, connection, and serendipity of in\-person/hybrid work culture (3\+ days/week) for builders who are committed to building truly innovative software and making a significant positive impact in the world.
##### Benefits
- Mission\-driven, values\-based culture.
- Competitive pay.
- Unmatched opportunities to learn and develop; front\-row seat at a fast\-growing tech startup
- Generous PTO, plus paid company holidays.
- Stock options.
- Medical, dental, and vision options.
- 401(k)
- Free Employee Assistance Program
- Parental Leave Program
- Pet Insurance
More About Us
Hearth embraces diversity. We are proud to be an equal opportunity workplace and do not discriminate on the basis of sex, race, color, age, sexual orientation, gender identity, religion, national origin, citizenship, marital status, veteran status, or disability status. We consider employment for qualified applicants with arrest and conviction records.
Hearth uses AI\-assisted tools as part of our hiring process. All hiring decisions are made by a Hearth employee. Questions about our process? Contact [email protected].
The pay range for this role is:
180,000 \- 200,000 USD per year(New York)
Salary Context
This $180K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Hearth, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($190K) sits 5% above the category median. Disclosed range: $180K to $200K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Hearth AI Hiring
Hearth has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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