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About This Role
Computer Vision \& Machine Learning Developer
This role is posted as a hybrid opportunity, blending in\-office and remote work.
Tommy Car Wash Systems is looking for a Computer Vision \& Machine Learning Developer who enjoys building intelligent systems that operate in the real world. This role sits at the intersection of machine learning research and production software engineering. You will work on problems such as optical character recognition (OCR), license plate recognition, and CNN\-based visual perception systems, while also helping design and deploy computer vision applications that run in live operational environments.
What can Tommy's offer you?
- Base pay and eligibility for annual profit\-sharing bonus
- Full insurance package including Health, Dental, Vision, Life, Disability, Employee Assistance
- Dependent Care FSA with on\-site Daycare options
- 401k match and complimentary financial planning services
- Paid time off and paid holidays
- Opportunity for continued education and tuition assistance
- Valuable learning and development program
- Significant ability to grow internally for motivated and strong performing team members
- Fun, energetic, family\-oriented work culture with an emphasis on team member morale
- Growing nationwide brand / presence
Position Responsibilities:
- Develop machine learning and computer vision systems for real\-world applications.
- Design, train, and evaluate CNN\-based models for visual recognition tasks.
- Build production software that integrates ML models into larger applications and pipelines.
- Implement real\-time or near\-real\-time processing of image and video data.
- Prototype and test new approaches to perception problems using experimental data.
- Contribute to data engineering tasks including dataset creation, labeling, and preprocessing.
- Deploy models to edge devices or cloud infrastructure and maintain their performance in production.
- Write clean, maintainable code using modern software engineering practices.
- Collaborate with engineers, product teams, and stakeholders to translate operational needs into intelligent systems.
- Other duties as assigned; duties and responsibilities may change at any time with or without notice.
Applied Machine Learning Systems:
- OCR and license plate recognition pipelines
- CNN\-based detection and classification models
- Retail or operational analytics derived from visual data
- Data collection, labeling, and model evaluation workflows
Computer Vision Engineering:
- Real\-time video processing systems
- Object detection, tracking, and environmental monitoring
- Integration with camera hardware and edge compute devices
- Production deployment and system optimization
Position Qualifications \& Candidate Attributes:
- Bachelor’s or Master’s degree in Computer Science, Robotics, Machine Learning, Electrical Engineering, or related field (or equivalent experience).
- Strong programming skills in OOP language, preferably Python.
- Experience with machine learning frameworks such as PyTorch or TensorFlow.
- Experience working with computer vision or image processing techniques.
- Solid understanding of software engineering fundamentals including version control, testing, debugging, and modular design.
- Ability to move between rapid experimentation and production\-grade development.
- Views customer care as high priority; exhibits a positive can\-do attitude
- Displays a strong initiative and drive to identify gaps and fill them
- Experience building OCR or license plate recognition systems.
- Experience with convolutional neural networks for vision tasks.
- Familiarity with OpenCV or similar image processing libraries.
- Experience working with video pipelines or camera systems.
- Experience deploying models to edge devices (Jetson, Coral, etc.).
- Experience with Docker, Linux, and CI/CD workflows.
- Familiarity with C\+\+ or another performance\-oriented language.
- Experience working with cloud ML platforms or model lifecycle tools.
- Experience with cloud platforms (AWS, Google Cloud, Azure etc).
- Familiarity with Node.js, C\+\+, JavaScript, and other programming languages.
- Experience with PCs and camera hardware connections.
- Technical savvy and proficient in Microsoft Office; experience within database systems a plus
- Excellent written and oral communication skills
- Process\-oriented and strong collaborator with ability to communicate and manage well at all levels of the organization and across various departments
- Strong organizational and time management skills; ability to multitask and prioritize workload
- Highly adaptable with strong problem\-solving and critical thinking skills; ability to exercise good judgment and make sound data\-backed decisions
- High level of integrity and dependability with a strong sense of urgency and results\-orientation
Work Environment and Physical Demands:
Office: This job operates in a professional office environment. Office hours are Monday through Friday from 8:00am \- 5:00pm. This role routinely uses standard office equipment such as computers, phones, photocopiers, filing cabinets and operates primarily indoors with limited to no travel expectation.
Overview of Tommy Enterprises Companies:
Tommy's Express is a national franchise for outstanding car washes and car care services. Powered by industry leading technology and decades of experience and planning, Tommy's Express car washes deliver a cutting\-edge car wash experience unlike anything you've encountered before. Our fully automatic washes feature advances including the easy\-loading car wash dual belt conveyor, wide open car wash bay for natural lighting, advanced presoak and sealer services, and free high\-power self\-serve vacuums on site.
Tommy’s Express Operations consists of a number of corporately owned Tommy’s Express car wash locations across the country. This is a quickly growing operation with intentions to open or acquire 3\-5 new locations per year through the launch of Tommy’s Express Capital, a new private fund strategy.
Tommy Car Wash Systems (“TCWS”) is the power behind our Tommy’s Express equipment. TCWS is a team of passionate car wash professionals working to create opportunities for our partners to become the best car wash operators they can be. We provide modular building designs, robust stainless\-steel car wash equipment, an advanced Wash Club license plate reader system, the Tommy Transporter belt, high performance wash detergents, and an industry\-leading franchise opportunity. At Tommy Car Wash Systems, we have a solution for almost any size operator. Our team has assisted in the development of hundreds of some of the most successful car washes around the world. Together, Tommy’s Express and Tommy Car Wash Systems make up the Tommy’s Corporate brand, headquartered in Holland, MI.
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 Tommy Car Wash, 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.
Tommy Car Wash AI Hiring
Tommy Car Wash has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Holland, MI, US.
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
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