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About This Role
Credit Acceptance is proud to be an award\-winning company recognized both locally and nationally across multiple workplace categories. Our world\-class culture is shaped by dedicated team members who are driven to succeed as professionals individually and together as a team. Backed by a strong product, exceptional people, and a stable financial foundation, we’ve grown into a leading provider of used and new car financing across the country.
Our Engineering and Analytics Team Members utilize the latest technology to develop, monitor, and maintain complex practices that help optimize our success. Our Team Members value being challenged, are encouraged to express their ideas, and have the flexibility to enjoy work life balance. We build intrinsic value by partnering with all functions of our business to support their success and make strategic business decisions. We focus on professional development and continuous improvement while enjoying a casual work environment and Great Place to Work culture!
We are seeking a highly motivated and experienced Staff MLE within AI team. The ideal candidate will have a strong technical background in decision science, machine learning, and generative AI with a proven track record in solving business problems and implementing large\-scale automated solutions in partnership with the respective engineering teams. In this role, you will partner with business and engineering stakeholders to formulate the vision to achieve the company’s strategic goals and co\-lead the roadmap to deliver innovative solutions for dealers, consumers and team members. As a Staff, MLE at Credit Acceptance, you will play a pivotal role in the success of this mission as you would lead the development of AI\-powered solutions across different business areas. This involves understanding the business processes, identifying new opportunities to add value using ML/AI algorithms and harnessing data sources to build state\-of\-the\-art ML/AI solutions.
Outcomes and Activities:
- This position will work from home; occasional planned travel to an assigned Southfield, Michigan office location may be required. However, this position is permitted to work at a Southfield, Michigan office location if requested by the team member.
- ML Outcomes:
+ Explore and apply advanced machine learning techniques, including not limited to large language models (LLMs), deep learning, and graph neural networks, to solve complex challenges across the organization.
+ Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans.
+ Drive execution and delivery of ML/AI solutions by managing priorities, deadlines, and deliverables, leveraging your technical expertise.
+ Design and deliver scalable, secure systems using state\-of\-the\-art AI/ML technologies and industry best practices, and nurture the culture of creating high\-quality, well\-tested systems to address critical product and business needs.
- Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency.
- Ensure the security, scalability, and architectural integrity of feature designs through reviews across teams.
- Deliver hands\-on solutions while mentoring other data professionals (including MLEs) within the organization
+ Guide a team of MLEs across different areas:
- Mentoring: Mentor team members on design principles, coding standards, and the adoption of AI productivity tools.
- Recommendations – Personalize guidance across different surfaces using deep learning methods; personalize layouts with Bayesian contextual multi\-armed bandits
- Growth: Foster long\-term growth through data\-driven causality and incrementality
- Gen\-AI: Power existing applications with Gen AI models and engineering to improve downstream experience and decisions
- Lifecycle \- Using ML models (such as XGBoost \& Causal Meta\-Learner\-based model, etc), proactively guide business teams across different areas
- Engineering \- With engineering partners, build ML and Gen\-AI platform and inference pipelines for different types of models
- Gen AI Outcomes:
+ Architect and implement enterprise\-grade LLM\-powered solutions, managing the full lifecycle from business requirements to production deployment, monitoring, and continuous optimization
+ Design and develop multi\-agent GenAI systems using state\-of\-the\-art frameworks (LangChain, LlamaIndex) to orchestrate complex workflows across retrieval augmentation, data operations, and compliance verification
+ Engineer robust Retrieval Augmented Generation (RAG) pipelines incorporating advanced techniques such as hybrid retrieval, reranking, query expansion, and contextual compression
+ Implement parameter\-efficient fine\-tuning strategies (LoRA, QLoRA, PEFT) to adapt foundation models to domain\-specific use cases while optimizing for inference costs and latency
+ Develop intelligent routing and orchestration systems to manage conversation state across multiple specialized AI agents, ensuring seamless transitions between different system capabilities
+ Build evaluation frameworks to measure and improve LLM performance across diverse metrics, including factuality, coherence, task completion, and alignment with business objectives
+ Integrate LLM solutions with existing enterprise architecture, ensuring compliance with data security policies, authentication mechanisms, and transaction safety requirements
Competencies: The following items detail how you will be successful in this role.
- Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer’s shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer\-centric experience.
- Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions.
- One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively.
- Owner’s Mindset: Owner’s Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business.
Required:
- PhD in Computer Science, Stats, Economics, or a relevant technical field with at least 5\+ years of relevant experience or MS with at least 8\+ years of experience in machine learning and software engineering
- ML Skills: 6\+ years of hands\-on experience designing, building and deploying AI (ML, DL, Gen\-AI) models, including Reinforcement Learning algorithms, Recommendation systems, Transformers, fine\-tuned LLMs, Causal Inference, Regressions, etc., with a solid understanding of mathematics, statistics, and engineering needed to build such infra
- GenAI Skills: 4\+ years of experience building and deploying AI/ML applications including Reinforcement algorithms, Recommendation systems, Generative AI etc. with solid understanding of mathematics, Computer Science, foundation concepts and engineering behind building AI applications and LLMs
- Experience applying agentic AI to design and implement scalable multi\-agent systems
- Strong problem\-solving skills with bias for action
Preferred:
- ML Preference:
+ Experience in the automotive industry, especially in building ML/AI systems while ensuring local and central regulations
+ Experience in model interpretability and responsible AI practices.
+ Expertise in data science, advanced experimentation and visualization techniques.
+ Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray)
+ Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints
+ Experience with Databricks MLflow for ML lifecycle management and model versioning
+ Hands\-on experience with Databricks Model Serving for production ML deployments
- GenAI Preference:
+ Demonstrable experience in parameter\-efficient fine\-tuning, model quantization, and quantization\-aware fine\-tuning of LLM models
+ Hands\-on knowledge of Chain\-of\-Thoughts, Tree\-of\-Thoughts, Graph\-of\-Thoughts prompting strategies
+ Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray)
+ Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints
+ Experience with Databricks MLflow for ML lifecycle management and model versioning
+ Hands\-on experience with Databricks Model Serving for production ML deployments
+ Knowledge of multimodal AI (text, image, audio integration)
+ Proficiency with GenAI frameworks/tools and technologies such as Apache Airflow, Spark, Flink, Kafka/Kinesis, Snowflake, and Databricks.
- Demonstrable experience in parameter\-efficient fine\-tuning, model quantization, and quantization\-aware fine\-tuning of LLM models
- Hands\-on knowledge of Chain\-of\-Thoughts, Tree\-of\-Thoughts, Graph\-of\-Thoughts prompting strategies
Knowledge and Skills:
- ML Requirements:
+ Hands\-on expertise in scaling and maintaining production\-grade ML services, with a strong focus on ML/LLM Operations (versioning, automation, observability, automated training and monitoring, etc.) and ability to balance ML model complexity with production requirements
+ Passion for identifying new business opportunities and experience of using a test and learn approach to bring scalable and efficient solutions integrating AI algorithms, ML/LLM Ops, and s/w engineering
+ Experience partnering with the engineering, product, business operations, legal and other teams while designing, building, and executing solutions
- Gen AI Requirements
+ Proficiency with model training/inference frameworks (PyTorch, TensorFlow, Hugging Face Transformers)
+ Experience building conversational AI (Text, Voice) , content generation, or code generation systems
+ Hands\-on experience with building, fine\-tuning and deploying multi\-modal LLM Models and managing the end\-to\-end model lifecycle
+ Experience partnering with engineering, product, BizOps and other data teams while designing, building and executing solutions
+ Deep understanding in at least three of the following areas: data mining, advanced statistics, machine learning, deep learning (incl NLP)
Target Compensation: A competitive base salary range from $153,759 – $225,514\. This position is eligible for an annual variable bonus of cash and equity, between 10 \- 20%. Bonus amounts are based on individual performance. Final compensation within the range is influenced by many factors including role\-specific skills, depth and experience level, industry background, relevant education and certifications.
Candidates who reside in the following major metropolitan areas may be eligible for a premium on top of the posted range based on their specific zone: San Francisco, Seattle, Boston, New York City, Los Angeles and San Diego.
INDENGLP
\#zip
\#LI\-Remote
Benefits
- Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/ dental/vision and many nonstandard benefits that make us a Great Place to Work
Our Company Values:
To be successful in this role, Team Members need to be:
- Positive by maintaining resiliency and focusing on solutions
- Respectful by collaborating and actively listening
- Insightful by cultivating innovation, accumulating business and role specific knowledge, demonstrating self\-awareness and making quality decisions
- Direct by effectively communicating and conveying courage
- Earnest by taking accountability, applying feedback and effectively planning and priority setting
Expectations:
- Remain compliant with our policies processes and legal guidelines
- All other duties as assigned
- Attendance as required by department
Advice !
We understand that your career search may look different than others. Our hiring team wants to make sure that this would be a fit not just for us, but for you long term. If you are actively looking or starting to explore new opportunities, send us your application!
P.S .
We have great details around our stats, success, history and more. We’re proud of our culture and are happy to share why – let’s talk!
Required degrees must have been earned at institutions of Higher Education which are accredited by the Council for Higher Education Accreditation or equivalent.
Credit Acceptance is dedicated to providing a safe and inclusive working environment for all. As part of our Culture of Compliance, we are proud to be an Equal Opportunity Employer and value our culturally diverse workforce. All qualified applicants will receive consideration for employment regardless of the person’s age, race, color, religion, sex, gender, sexual orientation, gender identity, national origin, veteran or disability status, criminal history, or any other legally protected characteristic.
California Residents: Please click here for the California Consumer Privacy Act (CCPA) notice regarding the personal information Credit Acceptance may collect from you.
Salary Context
This $153K-$225K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Credit Acceptance, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $153K to $225K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Credit Acceptance AI Hiring
Credit Acceptance has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $225K - $225K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% of all AI roles offer remote work.
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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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