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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 building an extremely data \& AI first Customer Strategy team within our Business Operations function at Owner. BizOps doesn't just analyze the business — we ship the products that drive it. The CS analytics function has already built churn risk signals, CSM engagement scoring, and Support analytics that shape how the company invests in retention. And we're just getting started.
CS is at an inflection point: new product lines, AI\-led engagement, plans to branch into new verticals, and a growing book of business that needs to scale beyond what CSMs can cover manually. This role will be at the center of designing our strategy using a data\-backed approach, and leading the follow\-through.
As a Lead, Customer Strategy Analytics \& Applied AI, you'll be a senior contributor who drives analytics and strategy end\-to\-end for the CS org. You'll work directly with the SVP of CS and the broader CS leadership team to identify the highest\-impact opportunities, then build the analyses, tools, and AI\-powered systems that turn them into results.
This role is 100% remote and can be based anywhere in the US or Canada.
You should definitely apply if you…
- Have felt torn about whether to pursue a business vs. technical/analytical career path (at Owner we believe these must merge).
- Get more energy from building and shipping than from analyzing and presenting.
- Are excited about applied AI: building AI\-powered products, and using AI to change how analytics and building gets done.
- Get energized by new frontiers — defining the next version of CS strategy, not optimizing existing playbooks.
- When solving problems, dive deep to understand root causes and become a subject matter expert. You don't stop at surface\-level insights.
- Aren't afraid to pick up new tools and skills, from Claude Code to custom machine learning pipelines, whatever the problem requires.
- Want to build lasting systems and products, not one\-off analyses.
- Thrive in ambiguity and don't need a playbook to figure out what to work on next.
The impact you will have
- Own Customer Analytics: Improve the analytics, engagement and AI integration strategy for Customer Success and Customer Support. This isn't just an insights role \- you'll design and ship data products and automated AI workflows that enable teams to act on data without waiting for analysis.
- Strategize across the customer lifecycle: Work directly with the SVP of CS on our customer engagement model, org structure, and where CS should invest its hours — bringing rigor and a point of view to the biggest strategic questions.
- Direct the pod: Set the priorities and direction for CS analytics \+ applied AI work. Guide the work happening in your pod, partnering with Associates to shape analyses, recommendations and builds.
- Build AI\-powered tools that drive incremental value: Ship things like churn risk scoring, CSM next\-best\-action systems, automated retention playbooks, and AI eval pipelines that change how CSMs and Support operate.
- Accelerate impact with AI: Establish how CS analytics and the broader BizOps team uses AI to multiply output across analytics and operating workflows.
Who you'll work with
- Reporting Structure: This role reports to Nali Amin, Director of Business Operations – Customer \& Product.
- Cross\-functional Partners: This is the most cross\-functional team within Business Operations; you'll collaborate with Product, Sales, Launch, Enablement, and RevOps, and partner most closely with Customer Success.
- Technical Collaboration: You will collaborate with Analytics Engineers on all technical aspects, including data modeling, data quality, and the use of tools like DBT and Snowflake.
- Executive Visibility: This work directly drives our customer strategy, which means regular exposure to senior and executive leadership and close partnership with the SVP of Customer Success.
What we're looking for
- 5–7\+ years in analytics, strategy, or ops — whether that's management consulting, startup analytics, BizOps, or a hybrid path that's hard to label
- You've personally shipped data products, models, or AI\-powered systems into production — not just delivered insights
- Excellent numerical skills, with additional technical background or education strongly preferred. Claude (or equivalent agentic tooling) is a must; SQL/Python skills are a plus!
- Familiarity with how CS / Support orgs actually operate. Deep functional pedigree not required, but you should come up to speed quickly
- Self\-sufficient operator — can run an end\-to\-end workstream without scaffolding
- Player\-coach instinct: you take satisfaction in helping others do their best work. Formal management experience welcome but not required
- Comfortable with ambiguity; you don't need a playbook to figure out what to work on next
- Experience with SaaS, marketplaces, or high\-growth startups preferred
- Restaurant industry experience is a plus!
Pay and benefits
The estimated base salary range for this role is $190,000 – $210,000, 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 $190K-$210K 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 Train With Ellie, 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 ($200K) sits 10% above the category median. Disclosed range: $190K to $210K.
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.
Train With Ellie AI Hiring
Train With Ellie has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, OR, US. Compensation range: $192K - $230K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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