Job description

At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Outcomes and Activities Enterprise target architecture: Convene the principal and staff engineers who own each domain to define the end-state architecture across originations, servicing, marketplace, data, and AI, and use it to drive the multi-year plan. Ensure every component has a defined interface with stated inputs and outputs, every unmade decision has a written principle standing in for it, and the document states honestly how close the organization is to the end state. Publish the architecture, including the tradeoffs that were rejected and why. Agentic AI architecture across the enterprise: Own the enterprise agent architecture spanning servicing and collections, the dealer side, originations, and internal engineering and back-office workflows, built on one common foundation rather than one stack per team. Establish a governed way for agents to discover and call business capabilities, data, and tools, using the Model Context Protocol as the integration layer. Define an identity and authorization model that treats an agent as a first-class actor with its own least-privilege entitlements, along with observability, evaluation, and cost controls that work consistently for every agent. Set policy on what an agent may decide independently versus what stays with a person, and define where that line moves and on what evidence. Design the architecture to produce an examiner-ready record of what each agent saw, recommended, and touched, and set the evaluation and adversarial testing approach that makes agent outcomes defensible. Ensure the same foundation and controls apply to agents built by outside firms on the company s behalf. Migration off on-premises infrastructure: Own the technical strategy for migrating transactional and analytical workloads off on-premises infrastructure to the company s strategic targets — AWS for transactional workloads and Databricks for analytical workloads — determining what lifts and shifts, what replatforms, what gets re-architected on the way versus after, and what the steady-state cost looks like. Own the resilience and recovery architecture for the target state. Data platform architecture: Set the architecture for change-data-capture-based ingestion, streaming where warranted, and a modern warehouse with no job-scheduling dependency, moving the organization from largely batch analytical data movement to real time. Help settle the open question of who owns the transactional and analytical data models. Real-time decisioning, model serving, and experimentation: Architect for pricing and credit model and scorecard updates that drop in as a layer rather than requiring a multi-quarter rebuild, and for an experimentation platform that supports orders of magnitude more concurrent experiments than a conventional release process allows. Marketplace and search architecture: Own the architecture and interfaces for a unified vehicle inventory, consumer search, and lead routing platform, ensuring the data model supports more than one class of seller from the start. AI-native engineering practice: Define the golden path for how software is specified, generated, reviewed, tested, and deployed, including the analyzers and gates that enforce it, and measure the inputs to that practice rather than just the outputs. Hold the build-versus-buy bar for AI capability, building what is core competency and about to scale, and renting the rest. Performance and peak readiness: Own the instrumentation, baseline, and remediation architecture needed to meet the standard that no page takes more than one second. Own peak-event headroom, including understanding the limits of each system and maintaining tested degradation levers ahead of need. Operating approach: Move the organization from project-shaped systems toward owned platforms with stated contracts. Document every component s inputs and outputs, and be ready to argue why closely coupled components should not be consolidated into one. Work toward a two-week requirement-to-production iteration bar, treating architecture that cannot be delivered incrementally as unfinished. Name what comes off the plan whenever a change in direction is requested. Make the call and own the outcome on architectural recommendations. Treat security, privacy, auditability, and regulatory obligation as design inputs from the start in this regulated environment. Hands-on, not hands-off: This is not optional. Stay personally engaged in the technical work rather than directing it from a distance. Join design reviews, write proofs of concept, read code, and engage in incident analysis. This hands-on time does not replace the work the teams own, and it is not a fallback when delivery slips. Every engineer in the organization will read how you write, how you argue a design, how you handle being wrong, and how you treat engineers who disagree with you as the bar. Staff and principal engineers should be measurably better at this work for having worked with you. Success measures: A published target architecture the domain owners agree with and that the annual and long-range plans are actually written against; an enterprise agent foundation that internal teams and partners build on, with access controls, evaluation, and audit records in place; a credible, dated, costed migration path off on-premises infrastructure and demonstrated progress against it; measured improvement in chosen input metrics such as latency, availability, recovery objectives, deployment frequency, and experiment throughput; growth in the number of principal and staff engineers who are better at this work as a result of this role; and architectural decisions that stop being relitigated because the reasoning was written down and the interfaces were clear. Competencies 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 Minimum 10 years of software engineering experience or comparable depth of experience A track record of setting and delivering multi-year technical strategy at enterprise scale, as an individual contributor rather than through a reporting line. Depth in distributed systems architecture, and in at least two of: agentic or LLM-based systems in production; large-scale cloud migration of transactional systems; modern data platform and lakehouse architecture; machine learning platform and model serving; high-traffic consumer or marketplace applications. Architecture ownership of agentic or LLM-based systems that reached production, including how tool and data access were governed, how the system was evaluated, and what its failure modes turned out to be. Demonstrated ownership of a legacy-to-modern migration of systems the business runs on, including the sequencing, the cost case, and the failure modes, with production revenue at stake rather than only familiarity with the concept. Hands-on credibility today: actively reads code, writes proofs of concept, joins design reviews, and participates in incident analysis. Evidence of influence without authority across organizational boundaries, including work that changed direction at the executive level. Strong written communication; architecture here is carried by documents that other people can act on without the author in the room. Demonstrated ability to develop staff and principal engineers, and to raise the technical bar of engineers not directly managed. Experience delivering in a regulated industry, or a clear ability to work inside audit, security, and compliance constraints without treating them as an obstacle. Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California. Preferred Experience Recognized contribution beyond one employer, such as open source, standards work, publications, patents, or a sustained external technical presence. Consumer lending, auto finance, insurance, or payments experience; or large-scale distributed systems experience supporting a business-critical function in another high-stakes domain. The Model Context Protocol or comparable agent tool-integration standards, and identity and authorization design for non-human actors. AWS and Databricks at production scale. Experience standing up an experimentation platform, or moving an organization from tens to thousands of experiments per year. Experience building AI-native development practice across a large engineering organization, and measuring whether it worked. Marketplace, search, or inventory systems with more than one class of seller. Knowledge and Skills Distributed Systems Mastery: Technical mastery across distributed systems and software architecture, with deep knowledge of current and emerging technologies and their business implications. Enterprise Problem-Solving: Ability to solve enterprise-wide problems where no precedent exists and to own cross-team shared infrastructure. Strategic Independence: Ability to define and execute technical strategy for the entire organization, partner closely with executives, mobilize resources, and drive consensus across stakeholders at all levels. Visionary Leadership: Bold strategic thinking paired with curiosity, ingenuity, and resourcefulness; recognized industry-level thought leadership and a commitment to lifelong learning. Customer and Market Insight: Company-wide expertise in customer needs, using deep customer and market understanding to shape product and technical strategy and anticipate the impact of industry trends. Technical Writing: Ability to produce architecture documentation clear and complete enough for others to act on independently. Executive Communication and Influence: Ability to build consensus and influence direction across organizational and executive boundaries without formal authority. Talent Development: Ability to raise the technical bar of engineers across the organization, including staff and principal engineers who are not direct reports. Target Compensation: This position offers a competitive base salary, with a range of $279,000 - $390,600. This position is eligible for an annual variable incentive of cash and equity, between 20 - 60%. Actual amounts are based on individual performance. Final compensation within the range is influenced by many factors including assigned hub location, role-specific skills, depth and experience level, industry background, relevant education and certifications. This position follows a hybrid work model, offering two work-from-home days and three standard in-office days each week with the team and key stakeholders at a local office. INDENGLP #zip #LI-Hybrid 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 an inclusive environment for all. We are proud to be an Equal Opportunity Employer and value a culturally diverse workforce. We believe in ensuring all team members demonstrate mutual respect for one another. All qualified applicants will receive consideration for employment without regard to protected characteristics like age, race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. California Residents: Please click here for the California Consumer Privacy Act (CCPA) notice regarding the personal information Credit Acceptance may collect from you. Play the video below to learn more about our Company culture.

Requirements

Must-haves

Outcomes and Activities
Competencies
Bachelor’s degree in Computer Science, Information Systems, or closely related field of study; or equivalent work experience
Minimum 10 years of software engineering experience or comparable depth of experience
A track record of setting and delivering multi-year technical strategy at enterprise scale, as an individual contributor rather than through a reporting line.
Architecture ownership of agentic or LLM-based systems that reached production, including how tool and data access were governed, how the system was evaluated, and what its failure modes turned out to be.
Hands-on credibility today: actively reads code, writes proofs of concept, joins design reviews, and participates in incident analysis.
Evidence of influence without authority across organizational boundaries, including work that changed direction at the executive level.
Strong written communication; architecture here is carried by documents that other people can act on without the author in the room.
Demonstrated ability to develop staff and principal engineers, and to raise the technical bar of engineers not directly managed.
Experience delivering in a regulated industry, or a clear ability to work inside audit, security, and compliance constraints without treating them as an obstacle.
Candidates must reside in or be willing to relocate to one of the following hub locations: the greater Southfield, Michigan, area; the greater Bellevue, Washington, area; or the San Francisco Bay Area, California.

Nice-to-haves

Recognized contribution beyond one employer, such as open source, standards work, publications, patents, or a sustained external technical presence.
Consumer lending, auto finance, insurance, or payments experience; or large-scale distributed systems experience supporting a business-critical function in another high-stakes domain.
The Model Context Protocol or comparable agent tool-integration standards, and identity and authorization design for non-human actors.
AWS and Databricks at production scale.
Experience standing up an experimentation platform, or moving an organization from tens to thousands of experiments per year.
Experience building AI-native development practice across a large engineering organization, and measuring whether it worked.
Marketplace, search, or inventory systems with more than one class of seller.
Knowledge and Skills
Distributed Systems Mastery: Technical mastery across distributed systems and software architecture, with deep knowledge of current and emerging technologies and their business implications.
Enterprise Problem-Solving: Ability to solve enterprise-wide problems where no precedent exists and to own cross-team shared infrastructure.
Strategic Independence: Ability to define and execute technical strategy for the entire organization, partner closely with executives, mobilize resources, and drive consensus across stakeholders at all levels.
Visionary Leadership: Bold strategic thinking paired with curiosity, ingenuity, and resourcefulness; recognized industry-level thought leadership and a commitment to lifelong learning.

Application prep

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aiawsrestllmmachine learning

Platform: workday
Effort: long

Benefits

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Dental

Vision

401(K)

Parental Leave