Distinguished Software Engineer

Credit Acceptance Corporation•San Francisco, CA

1d•Hybrid

About The Position

Credit Acceptance is seeking a Distinguished Software Engineer to help shape enterprise technology strategy and modernization across engineering, data, and AI. The role is an individual-contributor position that sets technical direction across organizational boundaries and remains personally involved in engineering work, including design reviews, proofs of concept, code review, and incident analysis. The engineer will define a target architecture spanning major business domains and establish a shared, governed architecture for enterprise agents. This includes agent identity and least-privilege authorization, tool and data access, observability, evaluation, cost controls, human-decision boundaries, adversarial testing, and audit-ready records. The role also owns the strategy for migrating transactional and analytical workloads from on-premises infrastructure to AWS and Databricks, along with resilience and recovery architecture. Additional areas of ownership include modern data ingestion and warehouse architecture; real-time decisioning, model serving, and experimentation; a unified marketplace, vehicle inventory, search, and lead-routing platform; AI-native engineering practices; and performance and peak-readiness standards. The role treats security, privacy, auditability, and regulatory obligations as design inputs, promotes owned platforms with explicit interfaces, and works toward incremental delivery and a two-week requirement-to-production iteration bar. Success includes a published target architecture used in planning, a governed enterprise agent foundation, a dated and costed migration plan with demonstrated progress, measurable improvements in engineering and system metrics, and development of staff and principal engineers across the organization. The role follows a hybrid work model with two work-from-home days and three in-office days each week. Candidates must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a closely related field, or equivalent work experience.
Minimum 10 years of software engineering experience or comparable depth of experience.
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 the following: 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.
Ownership of agentic or LLM-based systems that reached production, including governance of tool and data access, evaluation, and understanding of failure modes.
Ownership of a legacy-to-modern migration of business-critical systems, including sequencing, cost case, and failure modes, with production revenue at stake.
Current hands-on technical credibility, including reading code, writing proofs of concept, joining design reviews, and participating in incident analysis.
Evidence of influence without authority across organizational boundaries, including work that changed direction at the executive level.
Strong written communication and ability to produce actionable architecture documentation.
Demonstrated ability to develop staff and principal engineers and raise the technical bar of engineers who are not direct reports.
Experience delivering in a regulated industry, or clear ability to work within audit, security, and compliance constraints.
Must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.
Required degrees must be from institutions of higher education accredited by the Council for Higher Education Accreditation or an equivalent accrediting body.

Nice To Haves

Recognized contribution beyond one employer, such as open-source work, standards work, publications, patents, or a sustained external technical presence.
Experience in consumer lending, auto finance, insurance, or payments; or large-scale distributed systems supporting a business-critical function in another high-stakes domain.
Experience with the Model Context Protocol or comparable agent tool-integration standards, and identity and authorization design for non-human actors.
Production-scale experience with AWS and Databricks.
Experience establishing an experimentation platform or moving an organization from tens to thousands of experiments per year.
Experience building AI-native development practices across a large engineering organization and measuring their effectiveness.
Experience with marketplace, search, or inventory systems supporting more than one class of seller.

Responsibilities

Convene principal and staff engineers across domains to define, document, publish, and maintain an enterprise target architecture and multi-year technical plan, including interfaces, unresolved decisions, rejected tradeoffs, and progress toward the target.
Own a common enterprise architecture for agents spanning business and internal workflows, including governed discovery and access to capabilities, data, and tools using the Model Context Protocol as the integration layer.
Define agent identity, least-privilege authorization, observability, evaluation, cost controls, human-versus-agent decision boundaries, adversarial testing, and examiner-ready audit records; apply the foundation and controls to internally built and externally developed agents.
Own the strategy for migrating transactional and analytical workloads from on-premises infrastructure to AWS and Databricks, including migration sequencing, replatforming and re-architecture decisions, steady-state cost, and target-state resilience and recovery.
Set data platform architecture for change-data-capture ingestion, streaming where warranted, and a modern warehouse without job-scheduling dependency; help determine ownership of transactional and analytical data models.
Architect real-time pricing and credit model and scorecard updates, model serving, and experimentation capabilities that support substantially more concurrent experiments.
Own architecture and interfaces for unified vehicle inventory, consumer search, and lead routing, with a data model that supports multiple classes of sellers.
Define the AI-native engineering golden path for specifying, generating, reviewing, testing, and deploying software, including enforcement analyzers and gates; measure practice inputs and set build-versus-buy standards for AI capabilities.
Own instrumentation, baselines, and remediation architecture to meet the standard that no page takes more than one second; manage peak-event headroom, system limits, and tested degradation options.
Shift the organization toward owned platforms with explicit contracts, documented component inputs and outputs, incremental delivery, and a two-week requirement-to-production iteration goal.
Make and own architectural recommendations, document decisions to prevent repeated debate, and identify work to remove from plans when priorities change.
Treat security, privacy, auditability, and regulatory obligations as architectural design inputs.
Remain hands-on through design reviews, proofs of concept, code reading, and incident analysis while modeling strong technical practice and developing staff and principal engineers.
Influence stakeholders and executives across organizational boundaries without relying on a reporting line.