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About This Role
Location: Remote - United States (available in select states) Employment Type: Full-time Experience: No prior professional experience required Education: High school diploma or equivalent Programming Experience: Not required
States we are open to hiring from - Alabama, Arkansas, Georgia, Idaho, Indiana, Iowa, Kansas, Kentucky, Louisiana, Mississippi, Montana, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Utah, West Virginia, Wisconsin, Wyoming
Start Your Career in Technology
We're looking for curious, sharp, highly motivated people who are excited about technology and already use AI as part of how they learn, think, research, and solve problems.
You don't need a computer science degree.
You don't need previous professional experience.
You don't even need to know how to program.
What matters much more to us is how you think, how quickly you learn, and how effectively you use the tools available to you.
If your first instinct when you don't know something is to open ChatGPT, Claude, Gemini, or another AI tool and start figuring it out, you'll probably feel at home here.
And if AI helped you survive a few homework assignments, you might already be our kind of candidate.
What We're Looking For
We're looking for someone who:
- Is curious and enjoys figuring out unfamiliar problems.
- Learns quickly and is comfortable teaching themselves new things.
- Regularly uses AI tools to research, learn, create, analyze, or solve problems.
- Can think critically about AI-generated answers instead of blindly accepting them.
- Communicates clearly and isn't afraid to ask questions.
- Takes ownership of their work.
- Is comfortable experimenting, making mistakes, and learning from them.
- Has a high school diploma or equivalent.
- Has an SAT score that can be verified as part of our hiring process, where available.
Programming experience is not required.
If you've experimented with programming, built something with AI, created a website, automated a task, or worked on your own technical project, that's a plus—but we care much more about your ability to learn than what you already know.
SAT Score Verification
Candidates who have taken the SAT will be asked to provide their SAT score and verify it using ShowMyScore as part of the application process.
SAT scores will be considered as one part of our overall assessment alongside other job-related qualifications.
What You'll Do
As a Trainee, you'll work alongside experienced team members while learning how a modern technology company operates.
Depending on the projects you're working on, you may:
- Use AI tools to research and solve real business and technical problems.
- Learn how software products are designed, built, tested, and improved.
- Test products and identify bugs or opportunities for improvement.
- Research new technologies and AI tools.
- Help create documentation and internal resources.
- Work with experienced developers and other team members.
- Learn programming and software-development fundamentals.
- Experiment with automation and AI-assisted development.
- Gradually take ownership of increasingly complex projects.
This isn't a classroom simulation. You'll be learning by contributing to real work.
Your Career Path
This Trainee role isn't designed to push everyone down the same career path.
You'll have the opportunity to learn about different parts of a growing technology company, work on real projects, and discover where your strengths and interests fit best.
Over time, your career could develop in areas such as:
- Software Development & Engineering
- AI & Automation
- Marketing
- Sales
- Sales Operations
- Salesforce
- Product
- Customer Success
- Research & Analytics
- or another area where your skills are a strong fit.
You don't need to know which path is right for you when you join. That's part of the point of the Trainee program.
As you work with different teams and take on different kinds of problems, we'll help you identify what you're good at, what you enjoy doing, and where you can make the greatest contribution.
For someone interested in technology, for example, a potential path might be:
Trainee → Junior Developer → Developer → Senior Developer
Someone else might discover that they're particularly good at marketing, sales, operations, product, or another function and build their career in that direction instead.
Our goal is to give strong performers opportunities to take on increasing responsibility and build a meaningful long-term career in the area where they're best suited.
Career progression and timing aren't guaranteed and will depend on individual performance, demonstrated skills, business needs, and available opportunities.
You Might Be a Great Fit If...
You've ever:
- You like to build physical things using a Raspberry Pi or build things in Minecraft using a Redstone.
- Used AI to teach yourself something completely new.
- Asked AI to help you understand something everyone else made sound complicated.
- Built something just because you wondered whether you could.
- Used ChatGPT, Claude, Gemini, or another AI tool almost every day.
- Found a repetitive task and thought, “There has to be an easier way to do this.”
- Started experimenting with coding even though nobody asked you to.
- Gone down a research rabbit hole because you became genuinely curious about something.
We don't expect you to arrive knowing everything.
We expect you to be unusually good at figuring things out.
Pay: $11.00 - $22.00 per hour
Work Location: Remote
Salary Context
This $22K-$45K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At breadwinner, 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 $211,950 based on 13,048 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $111,000. This role's midpoint ($34K) sits 84% below the category median. Disclosed range: $22K to $45K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
breadwinner AI Hiring
breadwinner has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in WV, US. Compensation range: $45K - $45K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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