Interested in this AI/ML Engineer role at Train With Ellie?
Apply Now →Skills & Technologies
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're Looking For You
Independent restaurants are in a fight for their survival, up against chains with unlimited marketing budgets, loyalty platforms, and enterprise tech teams. We exist to change that. We build AI\-driven technology that puts the power of a Fortune 500 tech stack into the hands of an owner\-operator running tables on a Tuesday night.
To get there, we need a new kind of builder.
The best builders we know move fluidly between product thinking, engineering, and design because they love the whole game. AI has fundamentally changed what one person can build with great taste, technical excellence, and genuine customer empathy — and we're building a team of those people.
You'll help us solve one of the most important problems in tech right now: how to use AI to work 10x more effectively. That means establishing Owner as a leader in practical AI adoption, improving how we build across product, engineering, and design, and raising the bar for how we serve customers.
You'll ship tools and workflows that eliminate busywork and make your coworkers' jobs measurably better — working alongside some of our most senior engineers and the GTM teams that want to move faster.
If you've been quietly doing parts of the PM's job and the designer's job while still writing code — and loving every minute of it — this role was written for you.
This role is 100% remote and can be based anywhere in the United States or Canada. For San Francisco\-based candidates, we have a new HQ in the Presidio for optional in\-person collaboration.
The impact you'll have
- Own problems, not tickets. Talk directly to restaurant operators. Find the real friction. Define what we should build next. Build it.
- Go end\-to\-end. Design the experience, architect the system, write the code, and own the outcome. You're accountable for the full arc from insight to shipped product.
- Move fast, deliver excellence. AI lets us build 10x faster than we could a few years ago. You use that leverage to drive product excellence.
- Shape our product direction. Your intuitions about what customers need should influence our roadmap, not just your next sprint. We want product thinkers, not ticket closers.
- Hold the bar for craft. Clean, maintainable code. Intuitive, beautiful UX. Independent restaurant owners deserve great software, and you'll be the one making sure they get it.
- Be a user whisperer. Build relationships with operators. Bounce ideas off them. Know them by name. Iterate with them in real time.
What we're looking for
- 3\+ years of experience building and shipping software products
- You've worn multiple hats: thought like a PM, built like an engineer, and cared about UX like a designer
- Strong TypeScript experience; comfortable across our frontend (React, Next.js) and/or backend (Node.js, MongoDB) stacks
- AI\-native in how you work: you use AI tools to move faster, write better code, and explore solutions you couldn't have reached alone
- A track record of shipping things end\-to\-end that you're proud of
- Genuine customer empathy: you've talked to users directly, not just read their support tickets
- Unusually high agency: you identify problems, make a call, and move without waiting to be asked
- Collaborative at your core: past colleagues in product, design, and business would speak highly of how you work with them
- You think in systems: you understand how your work fits the larger product vision, and you actively contribute to the technical frameworks the whole team builds on
- Experience at a product\-focused startup or high\-growth technology company
We'll figure out where you sit in the org as we get to know you. Some builders are more product\-oriented, some are more engineering\-oriented, and we're happy to meet you where you are.
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.
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. Mid-level AI roles across all categories have a median of $165,000.
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.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.