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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!
Put AI to work where it changes lives: We are looking for a Staff/Senior Staff Software Engineer for our Enterprise AI Enablement team. You will architect, build and ship the AI engineering systems that use state\-of\-the\-art AI capabilities to power our entire ecosystem – solving problems for dealers, consumers and team members. You will own these systems end to end, from the first conversation with the customer through production deployment, telemetry, and ongoing improvement.
Build for leverage: You will design tools, agents, and platform capabilities to be built once and adopted many times across the enterprise. Your goal is not one\-off automation, but reusable foundations that enable us to solve new problems faster, with greater consistency and less duplicated effort.
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.
- Sit alongside the customers, deeply understand their workflows, locate opportunities, and translate what you learn into impactful products
- Convert ambiguous problems into production\-ready solutions that are adopted across teams
- Architect, build, and deploy applied AI solutions across high\-value enterprise workflows e.g. automation, document intelligence, decision support, fraud intelligence, and intelligent assistants.
- Agent orchestration: Design and implement agentic orchestration systems that manage agent harnesses, context management, tool binding, reasoning steps, and business logic execution
- Knowledge retrieval: Apply modern AI retrieval architectures to index, embed, and surface relevant enterprise knowledge as a foundation for agent context
- Design and develop software and improve existing code to make it more efficient to detect bugs in the code
- Write unit\-tests and validate your software against acceptance criteria
- Evolve and transform the design and architecture of applications towards leading edge technologies and practices
- Author, apply and advocate for team coding, documenting and testing standards
- Conduct impact analysis to proactively identify impact of a change across multiple applications
- Learn the business process domain to better support the business and align technologies with the business process
- Experiment and test ideas, validate assumptions against needs, reach conclusions and recommend solutions
- Lead code reviews and communicate application changes
- Document code and projects so others can easily understand, maintain and support
- Debug the problems which arise in production and propose effective solutions within the application and across multiple applications
- Read, write and review design documents
- Contribute to team's sprint commitments and actively participate in our Agile practices, including recommendations for process improvement
- Lead continuous learning activities to improve design and code quality as well as to increase application domain knowledge
- Participate in the talent selection process
- Act as a mentor to guide and review the code, designs and documentation of less experienced software engineers
- Senior Staff: Act as a trusted technical advisor to senior leadership, anticipating evolving customer needs, framing risks and trade\-offs, and bringing clarity to where and how AI should be applied
- Senior Staff: Frame and drive build vs buy vs reuse decisions across the portfolio
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.
Requirements:
- Bachelor’s degree in Computer Science, Information Systems, or closely related field of study; or equivalent work experience
- Staff: Minimum 8 years of software engineering experience or comparable depth of experience
- Senior Staff: Minimum 10 years of software engineering experience or comparable depth of experience
- Experience in the lead role overseeing technical direction of a team of software engineering talent across multiple applications
- Demonstrated experience in engineering AI systems, including LLM\-driven and agentic architectures, orchestration frameworks, and tool integration
- Proven ability to rapidly prototype and take ideas from POC to MVP to production, building and launching net\-new products from the ground up
- Strong understanding and use of one or more object\-oriented programming languages and design patterns
- Practical experience in data modeling, design and messaging
- Experience working on mission\-critical enterprise class applications
- High ownership and ability to operate in ambiguous environments with a strong bias for action
- Demonstrated ability to coach and mentor less experienced team members
- Willingness to participate in an on\-call rotation
Preferred:
- Advanced understanding of IDEs, have the ability to navigate through them quickly and leverage advanced features to improve your performance
- Experience designing and building production AI retrieval systems (e.g. RAG, GraphRAG)
- Experience building and shipping generative AI systems in production, at scale.
- Deep expertise in operating AI systems in enterprise environments, including reliability, observability, guardrails, and compliance considerations
- Experience building context layers for agentic systems, including multi\-source blending, context window management, relevance scoring, and context quality evaluation across heterogeneous data sources
- Experience with AI\-oriented data architecture, including vector databases, embedding pipelines, feature stores, data contracts, and knowledge representation patterns such as ontologies and semantic layers
Knowledge and Skills:
- Ability to challenge the status quo and influence stakeholders to create innovative solutions
- Be collaborative with other team members, seeking a diversity of thought to meet business outcomes
- Ability to foster strong relationships across the organization
- Bring a strong understanding of relevant and emerging technologies, provide input and coach team members and embed learning and innovation in the day\-to\-day
- Experience and understanding of how to connect the work being done and how it drives business value
- Ability to communicate complex technical information (both verbal and written) to all levels, including senior leadership
Target Compensation Staff: A competitive base salary range from $154,837 to $227,095\. This position is eligible for an annual variable bonus of cash and equity, between 10\-20%. Bonus amounts are based on individual performance.
Target Compensation Senior Staff: A competitive base salary range from $182,601 to $267,814\. This position is eligible for an annual variable bonus of cash and equity, between 15\-30%. Bonus amounts are based on individual performance.
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 $154K-$227K range is above the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Credit Acceptance, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $154K to $227K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Credit Acceptance AI Hiring
Credit Acceptance has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Remote, US. Compensation range: $170K - $227K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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