Interested in this AI/ML Engineer role at Cox Automotive?
Apply Now →Skills & Technologies
About This Role
We are seeking a visionary Machine Learning Engineer Lead to spearhead our experimental ML initiatives and drive innovation across the organization. This role combines technical leadership in cutting\-edge research with the responsibility of building a culture of continuous learning and knowledge sharing. You'll lead efforts to identify, evaluate, and prototype emerging ML technologies while establishing our company as a thought leader in the ML community. Applies AI and Machine Learning (AI/ML) principles to design, test, and scale frameworks, systems, and models for big data predictive applications. Develops AI/ML\-powered solutions based on business needs. Researches, implements, and tests machine learning methods to create product features, automate workflows, extract insights from data, and improve data quality. Structures, trains, and deploys models to learn from complex data across multiple modalities (e.g., structured, unstructured, image, video, audio) to uncover patterns and develop practical solutions. Possesses deep knowledge in at least one sub\-area of machine learning, such as deep learning, generative AI, computer vision, optimization, predictive models, or causal machine learning.
WHAT YOU'LL DO
Key Responsibilities
- Accelerate ML development using AI tools for code generation, feature engineering, optimization, and validation
- Stay up to date with advancements in ML, AI, and emerging technologies
- Design, build, and maintain ML models, algorithms, and robust pipelines for data processing, training, and inference
- Optimize model performance, scalability, and reliability in production environments
- Collaborate cross\-functionally to translate model insights into business value and communicate project updates
- Contribute to ML infrastructure improvements, best practices, and documentation
- Partner with engineering teams to integrate AI\-enhanced models and establish automated monitoring frameworks.
- Establish AI governance practices including bias detection, interpretability, compliance monitoring, and responsible deployment.
- Mentor teams in AI adoption, share best practices, and promote responsible AI innovation culture.
- Lead AI transformation initiatives including tool evaluation, governance development, and strategic adoption planning.
- Analyzes complex data sets to solve real\-world business and customer use cases.
- Performs end\-to\-end development of machine learning models
- May assist with or lead the development of industry whitepapers or other technical publications.
- Continuously evaluate AI processes for accuracy, efficiency, and business impact while staying current on emerging technologies.
Design agentic workflows for autonomous training, data pipelines, and analytical problem solving appropriate to experience level.
*
Key AI Use Cases
- AI\-Accelerated Model Development: Use GitHub Copilot, Claude Code for rapid ML prototyping, automated feature engineering, and intelligent hyperparameter optimization.
- Agentic ML Workflows: Understand and deploy (P4\+) AWS AgentSquad, AWS Strands, LangChain agents for autonomous training pipelines, multi\-step analysis, and collaborative research.
- AI\-Enhanced Model Interpretation: Build on traditional frameworks (SHAP, LIME) with AI tools for enhanced stakeholder communication and automated insights.
- AI\-Powered Research: Leverage manual/autonomous competitive intelligence and research acceleration tools for methodology discovery and algorithm innovation.
WHO YOU ARE
Required Skills
- Proficiency in AI development tools (GitHub Copilot, Claude, GPT\-4\) for ML development with ability to validate AI outputs for production readiness.
- Understanding of agentic frameworks (AWS AgentSquad, AWS Strands, LangChain, agent patterns) with progression from basic configuration to custom enterprise system design.
- Knowledge of AI ethics, responsible AI practices, and governance frameworks for business\-critical ML deployment.
- Ability to leverage AI like Co\-Pilot for technical communication to stakeholders and cross\-functional collaboration.
Commitment to continuous learning in AI\-augmented data science and responsible AI use.
*
Required Qualifications
- Applicants must currently be authorized to work in the United States for any employer without current or future sponsorship. No OPT, CPT, STEM/OPT or visa sponsorship now or in future.
- Bachelor's degree in a related discipline and 6 years' experience in Machine Learning; or a different combination, such as a master's degree and 4 years' experience; a Ph.D. and 1 years' experience in a related field; or 18 years' experience in a related field with no degree
- Minimum of 6 years of experience as a Machine Learning Engineer or equivalent
- Deep expertise in multiple ML domains and familiarity with emerging research areas
- Strong experience in technology evaluation, competitive analysis, and strategic planning
- Comfortability with non\-deterministic systems
- Product background\- understand how to prioritize, collaborate across teams, manage dependencies with others, set strategy
- Experience in Rally, Jira or similar tools
- Skilled in analytical thinking, consulting, requirements analysis, system and technology integration and technology savvy.
- Skilled in collaborating with intent, communicating with impact, developing trust, driving innovation and striving for excellence.
- Proven track record of leading innovative projects from concept to proof\-of\-concept
- Demonstrated success in knowledge sharing and thought leadership (publications, speaking, etc.)
- Experience building and leading high\-performing research or innovation teams
- Excellent communication skills for technical and executive audiences
- Strong network within the ML research community
- Experience with research collaboration and partnership development
- Other duties as needed or required
- Must be comfortable with change and an evolving environment
Preferred Qualifications
- Experience in corporate research labs, innovation teams, or technology consulting
- Track record of identifying and successfully implementing breakthrough technologies
- Background in technology transfer from research to business applications
- Strong presence in the ML community (conference speaking, open\-source contributions, etc.)
- Knowledge of emerging areas such as LLMs, Agents, foundation models, multimodal AI, or quantum ML
Leadership Expectations
- Foster a culture of experimentation, learning, and calculated risk\-taking
- Drive consensus on research priorities while maintaining innovation velocity
- Develop talent through mentoring in both technical skills and research methodologies
- Communicate complex experimental results and strategic implications to all organizational levels
- Lead by example in intellectual curiosity, scientific rigor, and knowledge sharing
- Build bridges between cutting\-edge research and practical business applications
- Establish the team as a recognized center of excellence in experimental ML
USD 134,900\.00 \- 224,900\.00 per year
Compensation:
Compensation includes a base salary in the range of $134,900\.00 \- $224,900\.00\. The base salary may vary within the anticipated base pay range based on factors such as the ultimate location of the position and the selected candidate's knowledge, skills, and abilities. Position may be eligible for additional compensation that may include an incentive program.
Benefits:
The Company offers eligible employees the flexibility to take as much vacation with pay as they deem consistent with their duties, the company's needs, and its obligations; seven paid holidays throughout the calendar year; and up to 160 hours of paid wellness annually for their own wellness or that of family members. Employees are also eligible for additional paid time off in the form of bereavement leave, time off to vote, jury duty leave, volunteer time off, military leave, and parental leave.
EOE, including disability/vets
Salary Context
This $134K-$224K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Cox Automotive, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $134K to $224K.
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.
Cox Automotive AI Hiring
Cox Automotive has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US. Compensation range: $224K - $224K.
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.