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About This Role
About Wonderly
Wonderly is a closed-loop, end-to-end system that self-improves on our customers’ revenue. We serve the home service industry ($1.3T), and can consistently double a new customer’s revenue within 90 days. We make all of our AI and software completely free - and only get paid when our customers make real money. We’re here helping real small businesses make a life, pay for their children’s college tuition, and live the American dream.
Pre-AI SaaS sold tools. AI companies claim they sell outcomes - task done, call answered, lead booked, ticket resolved - but calls or leads aren’t the outcomes that ultimately matter. Revenue is what ultimately matters, and today, none of the AI companies actually promise revenue. Wonderly changes that.
Our 70-person team (mostly in PDE) spent over a year building in stealth; our thesis is that - if you actually want to control the end result (revenue) of a business and deploy AI fast/cheaply, you can’t just build the sexy part (agents), you must also build the unsexy parts and own everything end to end: the system of record layer and the application layer. We built from scratch every software a home service business needs - replacing ServiceTitan, Squarespace, Ringcentral, Jobber, Square, Hubspot, and a dozen others applications. And we built every agent necessary - AI marketing, AI receptionist, AI voice, AI sales outreach, AI sales coaching, AI follow-ups, AI 3D design, AI finance, AI quoting, AI collections, and more - all embedded inside our own system with native data access. As a result, we can deploy dozens of production-ready Agents to our customers in under 24 hours with no FDEs, no integrations, and no data cleaning work.
We are the most outcome driven business model in the business because we’ve proven our system and AI Agents actually work in driving the end result - revenue. We are not trying to sell our customers a dream - each of our 10+ core AI Agents help improve our customers’ revenue by 5-20%.
Wonderly already has PMF and has gone from 0 1 (we are happy to share more over a call), with very clear moat and deep industry knowledge that took a team of 70 hardcore A-players 18 months to build (we are at least 2 years ahead of anyone else in the space). We believe Wonderly has the potential to be a $100B - $1T outcome. The market is massive: home service businesses across North America (a $1.3T market). We’ve raised over $50M from top-tier investors, have $20M a year in free cashflow from another product line (Motion), and all that money gets pumped into Wonderly.
"AGI"
So you’ve been token maxxing and spending $4,000 a month on AI subscriptions. You’re vibe coding to the max, and sleeping barely 5 hours a day. You’ve embraced the polyphasic sleep cycle, so you can wake up in the middle of the night and press ‘Enter’ a couple times (I think that’s called harness engineering these days). And at the end of it, what exactly have you shipped? Have you made anyone’s life better?
If you are focused on technology purely for the sake of technology, then this is not the company for you. If you cannot tie your technology to real, obvious, business results, then this is not the company for you. In a world of slop factories, Wonderly had a radical idea: the purpose of software is to make someone’s life better. And in return, they pay you money.
Crazy, I know.
We are ruthlessly focused on helping small business owners in middle America put food on the table, send their children to college, and live the American Dream. And our customers love us for it.
Wonderly gives Main Street the AI Agents, capital, and revenue engine they need to scale, and charges nothing for any of it. We only get paid when our customers make real money. No surprise fees or gotchas.
Who You Are
You are a technologist at heart.
- Your dream vacation is tinkering and building something new. Every couple months you have a new side project. If you do not tinker, you cannot have taste.
- You are someone who keeps up with technology for fun. If I pulled up your youtube feed, it would be littered with tech talks.
- You explore new ways of building purely for the craft.
- You’re able to work startup hours because work is fun. This is the type of career someone would have to pay you to not do.
You’re someone who’s guided engineering orgs before, and you know the importance of a steady hand. That doesn’t mean you’re afraid to make big calls when necessary.
You’re someone who’s thought deeply about engineering strategy. You have strong opinions about the SDLC, and how it changes in the age of AI.
Above all, you’re deeply practical. The world is a messy place. You work with the world how it is, not how it should be.
The Role
Wonderly is internally powered by the first enterprise grade dark software factory. We have 50 internal tools (and growing) built this way, and we are rapidly improving our own development methods based on feedback from this development process. We are extremely reluctant to use external vendors; with our internal tools factory, we have replaced ~50 vendors (and more will be replaced soon). We do this for three main reasons:
- Margin
Software is entering a low-margin era. Most of our customers’ margins are only 30-40%, and as a result, we cannot pay frivolously for vendors that we could easily replicate in-house ourselves.
With AI, the cost of building – and yes, maintaining – these tools in-house has dramatically decreased. Every dollar that we can save is a dollar more we can invest on behalf of our customers.
- Operational excellence
Stitching together well known tools can quickly bootstrap you to an MVP in terms of business and engineering processes, but as you scale, you’ll find yourself tied down to the lowest common denominator of what each of these tools allow you to do. As a result, you’ll naturally adopt sub-optimal processes, and this will drive down your operational excellence.
Every operational mistake costs our customers real money. We cannot afford for this to happen. As we rapidly scale, we must have maximum flexibility to choose the best process first, and build the tooling to enable that process after the fact.
- Software revolution
As AI continues to eat software and its production lifecycle, we need to have a semi-production environment to push the boundaries. We need a place where we can experiment without risking customer outages. I say semi-production because everything can work in a toy sandbox environment. Our internal tools on the other hand have real customers, real traffic, and numerous edge cases. It is complex enough to give a real sense of whether this will work without immediately putting our end customers at risk.
Many of the architectural and AI coding decisions we took in production were first started in our internal tools test bed.
Your role is the ultimate culmination of both the tinkering in internal tools and the maturity of actual production. We need both to co-evolve with each other. We’re looking for someone who is bringing technical excellence (real money is on the line here!) first and foremost, but can also peer into the future for what’s coming and position Wonderly for the AI native era.
You are capable of holding two opposing view points simultaneously: AI is going to change everything, and yet, most of what it produces today continues to be slop.
And your answer to the above paradox is ultimately going to be your roadmap in this company.
Wonderly is committed to creating a diverse and inclusive workplace where everyone is treated with respect and given equal opportunities. We do not discriminate based on gender identity, race, ancestry, disability, religion, sexual orientation, pregnancy status, veteran status, or any other characteristic protected by law. We welcome and encourage all qualified candidates to apply for our job openings.
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 Wonderly, 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.
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.
Wonderly AI Hiring
Wonderly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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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