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About This Role
8116 - Midtown Office - 2220 W. Broad Street, Richmond, Virginia, 23220 CarMax, the way your career should be!
Position Overview
As a Sr. Engineer, AI & ML, you will play a pivotal role in designing and implementing advanced Generative AI solutions that elevate CarMax’s omni-channel customer experience. You will leverage expertise in large language models (LLMs), semantic search frameworks, and AI orchestration to deliver innovative, scalable applications. Your work will directly influence CarMax’s technology evolution, ensuring secure, high-performing AI systems that drive business impact.
Why CarMax?
At CarMax, we are the nation’s largest retailer of used cars with stores from coast to coast, and we are still growing. We’re rethinking the way people buy cars – and it’s our associates that help us do just that. We believe work should feel meaningful and rewarding, with opportunities to make an impact every day. This is where innovation meets passion – be inspired and supported to take us to the future.
Team Overview
You’ll join CarMax’s GenAI team, collaborating with engineers, product leaders, and stakeholders to build next-generation AI capabilities. This team focuses on creating enterprise-grade AI solutions that enhance customer interactions and streamline business processes through cutting-edge machine learning technologies.
Role Responsibilities
- Design, develop, and deploy Generative AI models, including LLMs and semantic search frameworks, for customer-facing solutions.
- Identify and address implementation challenges, providing creative, technically sound solutions.
- Build integrated monitoring systems to validate AI performance and ensure architecture integrity.
- Mentor engineers on best practices for Generative AI development and deployment.
- Review code for quality and adherence to clean coding standards.
- Collaborate on prompt engineering, testing, and evaluation using tools such as PromptFlow.
- Drive continuous improvement in AI architecture through innovation and stakeholder feedback.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
- 5+ years of experience in AI/ML, cloud computing, or systems engineering.
- Advanced proficiency in Python and scripting languages (e.g., shell scripts).
- Hands-on experience with Azure OpenAI Service and Azure AI Search.
- Expertise in LLMs, semantic search frameworks, and AI orchestration layers (e.g., Semantic Kernel).
- Experience with DevOps practices, CI/CD pipelines, and testing frameworks.
- Strong communication skills and ability to influence technical vision across teams.
Preferred Qualifications
- Master’s degree in Computer Science, Engineering, or related field.
- Experience integrating CI/CD pipelines for prompt engineering with feedback loops.
- Knowledge of advanced Generative AI development tools and frameworks for enterprise-scale applications.
Work Location and Arrangement: This role will be based out of the CarMax Midtown office, Richmond VA or CarMax Technology Hub, Plano TX and have a Hybrid work arrangement.
- Associates based in Richmond work onsite 5 days per week.
- Associates based in Plano work onsite 2 days per week.
Work Authorization: Applicants must be currently authorized to work in the United States on a full-time basis. Sponsorship will not be considered for this specific role.
About CarMax
At CarMax, we revolutionized the used car buying experience over 30 years ago by introducing transparency and integrity into the process. Our commitment to customer experience, innovation, and community has made us the nation’s largest used car retailer. With over 250 store locations and over 30,000 associates, we are proud to have been recognized as one of the Fortune 100 Best Companies to Work For® and are committed to helping our communities thrive.
As an associate, you are part of an innovative movement to empower the modern customer and drive progress. Your work fuels change—sparking ideas, overcoming challenges, and shaping what’s next. Join us in creating a better future– for our company, our customers, and the communities we call home.
CarMax is an equal opportunity employer, and all qualified candidates will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, genetic information, national origin, protected veteran status, disability status, or any other characteristic protected by law.
The annual salary for this position is:
$120,000.00 - $180,000.00May be eligible for bonus and equity.
Benefits:
Except as otherwise required by state law, CarMax Associates are entitled to the following paid sick, vacation, and holiday time.
Associates that are considered full-time hourly or commission/incentive eligible:
- To earn up to 48 hours of sick time per year accrued on a per pay period basis and between 80 hours and 200 hours per year of vacation time after a 90 day waiting period depending on years of continuous service with the Company.
- For 8 hours of pay for each of a total of 6 paid scheduled holidays per year plus 1 floating holiday. If such an Associate does work on a scheduled holiday due to business need, they are eligible for Holiday Premium Pay.
Associates considered full-time salaried are entitled to paid time away with no specified limit as needed for sick, vacation, bereavement, jury duty, holidays, floating holiday, etc. subject to manager approval.
For more details about benefits, please visit our CarMax Benefits website.
Upon an applicant's request, CarMax will consider reasonable accommodation to complete the CarMax Job Application.
Salary Context
This $120K-$180K range is below the median 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 CarMax, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $120K to $180K.
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.
CarMax AI Hiring
CarMax has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Richmond, VA, US. Compensation range: $180K - $180K.
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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