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About This Role
About Owner
Owner is the AI\-native system local business owners use to succeed, starting with restaurants.
We’re building the system that replaces the many tools owners use to run their business.
It powers everything from the restaurant’s website, online ordering, CRM, POS, and more.
Product philosophy
Most small business software makes owners do the work to get what they want: sales growth and profit growth. Owner does the work for them agentically.
Our system drives demand, converts it, and helps operators run their business day to day. As it improves, the business improves with it.
Using Owner should feel like having a team of great operators, engineers, and marketers working for you.
Our vision
We’re starting by helping independent restaurants succeed online.
But it’s not just restaurants that need our help. Most local businesses are struggling with these same problems. Huge technology corporations are taking their customers, bleeding their profits, and making it hard for them to survive.
Once we nail the solution for restaurants – we’ll scale it into every other local business type.
In the future we envision, tens of millions of local business owners will use our technology to succeed in the digital age.
Our traction
Since 2020, we've generated tens of millions in revenue and processed over a billion dollars of online orders. 1 in 5 Americans have used an Owner.com website.
More importantly, we’ve helped over 20,000 restaurant owners, and saved them nearly $200 million in fees.
Our team
Our team is now in the low hundreds. We’ve got top talent from the most successful companies in SMB software, including: Shopify, HubSpot, DoorDash, ServiceTitan, Rappi, Faire and Stripe.
We’ll be scaling even faster in 2026 to keep pace with our customer growth.
Where we work
Owner is a remote\-first, global company headquartered in San Francisco, with a sales hub in Toronto. For a few of our roles we prioritize in\-person collaboration at one of our office locations. Most of our teammates are distributed throughout the globe. Please review the role description and discuss with your recruiter for more details on location!
Why we are looking for you
We are scaling Owner's Applied AI organization. This is a fast\-paced team that works on the most valuable opportunities we can find anywhere in the business. As an Applied AI Lead, you'll be responsible for finding those opportunities in the first place: looking across everything happening at Owner, understanding where AI can have the most impact, and sizing the potential before a single line of code gets written. Then you'll build, ship, and iterate on the solution until the impact shows up.
Some of these opportunities create efficiency by cutting out manual work that no longer needs to be manual. Others create better outcomes entirely: processes that now perform far beyond what they used to, and things we always wanted to do that only became practical with AI. You'll chase both, and you'll be the one deciding which come first.
This role is 100% remote and can be based anywhere in the United States or Canada.
You should definitely apply if you…
- Care more about picking the right problem than about using the fanciest tool for it
- Love the hunt: scanning a business, finding what's broken or underpowered, and putting a number on the prize
- Get more energy from building and shipping than from analyzing and presenting
- Dive deep to understand root causes when solving problems, and become a subject matter expert along the way
- Aren't afraid to pick up new tools and skills, from Claude Code to custom ML pipelines, whatever the problem requires
- Want to see your work change the numbers, and feel unsatisfied until it does
- Thrive in ambiguity and don't need a playbook to figure out what to work on next
What we've built so far
- LLM\-powered binary classifier for restaurant concept fit with Owner's product. Ran on over 300k establishments in the US.
- Hyperpersonalized outbound email engine, sending around 5k unique emails daily with a best\-in\-class reply rate.
- Automated pre\-call research tool, eliminating the manual work that sales reps previously had to complete before every dial. Increased dial volume by almost double.
- Gradient Boosted Tree for predicting call pickup rate. Trained on dozens of features across tens of thousands of pieces of data, resulting in up to a \~2\.3x increased pickup rate for top\-identified leads. (ML experience is NOT required for this role; we used Claude Code to teach ourselves enough ML to build this.)
- Intelligent routing (iRouting) system incorporating our new pickup rate prediction score to optimize lead distribution on a daily basis.
The impact you will have
- You'll own the map of AI opportunity across the business: identifying problems and opportunities, dissecting and sizing them, and deciding what gets built next.
- The projects we work on are significant enough to alter the trajectory of the business. Recent builds have driven double digit improvements to company\-wide KPIs, and the backlog holds many more with similar potential.
- You'll carry each build from diagnosis through shipping, deployment, adoption, and iteration. A project is finished when the impact we set out for is visible, and you'll own that full arc.
- Many of the problems in our backlog are ambitious enough that they could be standalone products. We'll provide the tools and resources you need to execute them end to end.
Who you'll work with
- Reporting Structure: This role reports to Jonathan Shenkman.
- Team Collaboration: You'll work day to day with our Applied AI team and partner with key stakeholders across every team at Owner, including BizOps, RevOps, Sales, Marketing, Growth, Product, Finance, and Recruiting.
- Executive Visibility: Applied AI focuses exclusively on projects with the highest impact and ROI, which means regular exposure to senior and executive leadership.
What we're looking for
- A track record of finding the problems worth solving. You've looked at a messy business, figured out what actually matters, sized the prize, and picked the right thing to work on without anyone handing you a roadmap.
- Root\-cause instincts. You dig until you actually understand the problem instead of its symptoms. You rabbithole, but you also know when you've gone too deep.
- Hands\-on experience building with LLMs, integrating the models and APIs themselves: agents and assistants, structured output, evals, context engineering. You turn concepts into working code.
- 3\+ years of experience in a 0\-1 operations or startup role. We treat every build as a 0\-1 problem to solve from scratch.
- You move fast, work scrappy, and thrive in the chaos of an early\-stage startup environment, especially when things are ambiguous and timelines are tight.
- Comfortable taking ambiguity and turning it into a ruthlessly\-prioritized roadmap. You don't need a playbook to figure out what to work on next, which matters more than ever given how easy it has become to build almost anything.
- You treat shipping as the midpoint. You watch how a build performs in the real world, gather feedback, and iterate until the impact you set out for actually arrives.
- Strong communicator who can break down complex concepts for teammates, both human and AI.
- Restaurant industry experience is a plus!
Pay and benefits
The estimated base salary range for this role is $140,000 \- $180,000 USD, plus a generous pre\-IPO equity package
Other benefits include comprehensive health coverage, remote\-first workplace, unlimited PTO \- plus extra fun perks!
Notice \- Employment Scams
Communication from our team regarding job opportunities will only be made by an Owner team member with an @owner.com email address.
We do not conduct interviews over email or chat platforms, and we will never ask you to provide personal or financial information such as your mailing address, social security number, credit card numbers or banking information. If you believe you are being contacted by a scammer, please mark the communication as "phishing" or “spam” and do not respond.
Salary Context
This $140K-$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 Owner.com, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 26% below the category median. Disclosed range: $140K 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.
Owner.com AI Hiring
Owner.com has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, OR, US. Compensation range: $180K - $210K.
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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