Distinguished Software Engineer

Requirements:

• Bachelors degree in Computer Science, Information Systems, or a closely related field, or equivalent work experience.
• At least 10 years of software engineering experience or comparable depth of experience.
• Demonstrated success setting and delivering multi-year technical strategy at enterprise scale as an individual contributor, not through a reporting line.
• Deep expertise in distributed systems architecture and in at least two of the following: production agentic or LLM-based systems; large-scale cloud migration of transactional systems; modern data platform and lakehouse architecture; machine learning platforms and model serving; high-traffic consumer or marketplace applications.
• Experience owning agentic or LLM-based systems through production, including governance of tool and data access, evaluation, and understanding of failure modes.
• Experience leading a legacy-to-modern migration of business-critical systems, including sequencing, cost justification, and failure modes, with production revenue at stake.
• Current hands-on engineering practice, including reading code, building proofs of concept, participating in design reviews, and analyzing incidents.
• Evidence of influencing across organizational boundaries without direct authority, including changing direction at executive level.
• Strong written communication skills and the ability to create actionable architecture documents.
• Demonstrated ability to develop staff and principal engineers and raise the technical bar of engineers outside your direct management.
• Experience delivering in a regulated industry, or the ability to work effectively within audit, security, and compliance constraints.
• Must reside in or be willing to relocate to the greater Southfield, Michigan area.

Responsibilities:

• Bring domain-owning principal and staff engineers together to define and publish an enterprise target architecture across originations, servicing, marketplace, data, and AI; document interfaces, unresolved decisions and guiding principles, tradeoffs, and progress toward the target state.
• Own a common enterprise architecture for AI agents across customer servicing and collections, dealer operations, originations, engineering, and back-office workflows. Establish governed discovery and use of business capabilities, data, and tools through the Model Context Protocol.
• Define agent identity and least-privilege authorization, as well as consistent observability, evaluation, cost controls, autonomy policies, adversarial testing, and examiner-ready audit records. Apply the same foundation to agents built by external partners.
• Lead the strategy for moving transactional and analytical workloads from on-premises infrastructure to AWS and Databricks, respectively. Determine migration approaches, sequencing, re-architecture needs, steady-state costs, and resilience and recovery designs.
• Set the data platform architecture for change-data-capture ingestion, appropriate streaming, and a modern warehouse without job-scheduling dependencies; help determine ownership of transactional and analytical data models.
• Architect flexible real-time decisioning and model-serving capabilities for pricing and credit models and scorecards, along with an experimentation platform that supports substantially more concurrent experiments.
• Own the architecture and interfaces for a unified vehicle inventory, consumer search, and lead-routing platform, with a data model that supports multiple seller types from the outset.
• Establish an AI-native engineering path for specifying, generating, reviewing, testing, and deploying software, including enforcement tools and measures of practice inputs. Set build-versus-buy standards for AI capabilities.
• Lead performance instrumentation, baselining, and remediation to meet the one-second page-load standard; manage peak-event capacity, system limits, and tested degradation options.
• Shift the organization toward owned platforms with clear contracts and documented inputs and outputs. Support incremental delivery toward a two-week requirement-to-production cycle, identify work displaced by changing priorities, and take ownership of architectural decisions and outcomes.
• Treat security, privacy, auditability, and regulatory obligations as foundational design requirements.
• Stay personally involved in technical work through design reviews, proofs of concept, code review, and incident analysis, while leaving delivery ownership with the teams. Set a strong example in technical judgment, collaboration, and response to disagreement, and develop staff and principal engineers.
• Measure success through an agreed target architecture reflected in annual and long-range plans; an adopted, controlled, evaluated, and auditable agent foundation; a costed and dated infrastructure migration with demonstrated progress; improvements in selected measures such as latency, availability, recovery objectives, deployment frequency, and experiment throughput; stronger engineering capability; and clear decisions and interfaces that reduce repeated debate.

Technologies:

• AI
• AI Agents
• AWS
• Architect
• Cloud
• Databricks
• Support
• LLM
• Machine Learning
• Model Serving
• Security
• Agentic AI
• REST

More

At Credit Acceptance, we are building on a successful business while investing in technology, data, AI, and continuous improvement. Our engineering teams collaborate across the organization to solve complex challenges, create impactful solutions, modernize our systems, and support informed business decisions. We foster an environment where team members can contribute ideas, grow their careers, and help shape the future of the business. This is a hands-on enterprise architecture role in a regulated environment, based in or near our greater Southfield, Michigan hub.

last updated 43 week of 2026