Staff Software Engineer, Consumer 360

Credit Acceptance Corporation•San Francisco, CA

5d•$185,805 - $260,127•Hybrid

About The Position

Credit Acceptance is hiring a Staff Software Engineer for its Consumer 360 team. The role leads technical direction across multiple applications while designing, building, and operating cloud-native Java and Spring Boot services on AWS. It combines incremental modernization of existing systems with production AI-agent development and an AI-assisted software delivery model. The engineer is accountable for the correctness, security, maintainability, customer impact, and cost of the systems and AI-assisted work delivered by the team. The position uses spec-driven development, automated testing, API- and event-first design, and small, frequent releases. It includes migrating services and workloads to current runtimes and AWS, improving delivery flow, supporting production systems, documenting systems for both people and AI agents, and developing engineers through technical guidance and mentoring. The engineer also builds, evaluates, and operates production AI agents with appropriate guardrails, human oversight, observability, and cost controls. The role follows a hybrid work model: three standard in-office days and two work-from-home days each week. Candidates must reside in or be willing to relocate to the greater Southfield, Michigan area; the greater Bellevue, Washington area; or the San Francisco Bay Area, California. The stated base salary range is $185,805–$260,127, with eligibility for an annual cash and equity variable incentive of 10–20%.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a closely related field, or equivalent work experience.
At least 8 years of software engineering experience or comparable depth of experience.
Experience in a lead role overseeing the technical direction of a software engineering team across multiple applications.
Expert hands-on experience with Java 17 or 21 and Spring Boot, including production REST and event-driven services, object-oriented design, and design patterns.
Practical experience with AI-assisted delivery, spec-driven development, test automation, CI/CD in GitHub Actions, and small, frequent releases.
Practical experience in data modeling, design, and messaging.
Experience working on mission-critical enterprise-class applications.
Demonstrated ability to coach and mentor less experienced team members.
Willingness to participate in an on-call rotation.
Hands-on experience designing, deploying, and operating cloud-native AWS services, including Kubernetes containers such as Amazon EKS, GitOps, IAM, secrets management, observability, and reliability practices such as SLOs.
Hands-on experience designing, building, evaluating, and operating at least one LLM-powered agent in production, including secure tool use, retrieval where needed, evaluations, guardrails, observability, and human oversight for consequential actions.
Must reside in or be willing to relocate to the greater Southfield, Michigan area; the greater Bellevue, Washington area; or the San Francisco Bay Area, California.
Ability to challenge the status quo and influence stakeholders to create innovative solutions.
Collaborative approach that seeks diverse perspectives and builds strong relationships across the organization.
Understanding of relevant and emerging technologies, with ability to coach team members and embed learning and innovation into daily work.
Ability to connect work to business value.
Ability to communicate complex technical information verbally and in writing to all levels, including senior leadership.
Sound judgment about where AI should and should not act, including redesigning workflows before automating them, setting clear tool and action boundaries, keeping people involved in customer-impacting decisions, and evaluating AI output for accuracy, safety, prompt injection, data leakage, and bias.
Cost- and value-aware engineering, including understanding cloud and AI workload cost drivers and making evidence-based trade-offs among customer experience, reliability, speed, and spend.
For the degree requirement, degrees must be from institutions accredited by the Council for Higher Education Accreditation or an equivalent accrediting body.

Nice To Haves

Advanced use of AI-native development tools such as GitHub Copilot or Claude Code, including custom instructions, reusable skills and commands, hooks, and MCP integrations.
In-depth experience creating APIs and frameworks, including REST/JSON and legacy SOAP services; JUnit 5 and Mockito; WireMock; Pact consumer-driven contract testing; and GitHub with GitHub Actions.
Experience with API security, analytics, throttling, caching, logging, and request and response modifications.
Experience with API-first design, including versioned OpenAPI and event schemas agreed before coding and published for consumers, people, and agents.
Ability to use DDL to create database objects and relationships, write advanced queries, and optimize query performance.
Financial services industry experience.
Experience with customer data in a regulated business, including customer master data and identity resolution, consent and privacy-request fulfillment, and sensitive exchanges such as credit-bureau reporting and disputes (e.g., Metro 2 and e-OSCAR).
Experience with AI agent platforms and frameworks such as Amazon Bedrock, Bedrock AgentCore, Spring AI, or LangChain4j; Python agent frameworks are a plus.
Experience with LLM evaluation, guardrail, and observability tooling.

Responsibilities

Design, build, and continuously improve cloud-native Java 21 and Spring Boot services on AWS, using AI coding agents while owning the correctness, security, and maintainability of changes.
Use spec-driven development, with written specifications, acceptance criteria, requirement IDs, and automated unit, integration, and consumer/provider contract tests.
Lead incremental modernization of Consumer 360 systems, including runtime upgrades, migration from ActiveMQ to managed event-driven messaging, and migration of on-premises workloads and databases to AWS using safe cutovers.
Author, apply, and advocate for team coding, documentation, and testing standards.
Conduct impact analysis to identify how changes affect multiple applications.
Learn business processes and align technology with business needs; experiment, validate assumptions, and recommend solutions.
Lead code reviews and communicate application changes.
Document systems—including READMEs, agent instruction files, API and event specifications, and decision records—so people and AI agents can build, test, run, and safely change them.
Debug production problems and propose solutions within and across applications.
Read, write, and review design documents.
Own the end-to-end delivery flow and improve requirement-to-production time by addressing bottlenecks in specifications, reviews, environments, pipelines, and cross-team dependencies.
Lead continuous learning to improve design and code quality and increase application-domain knowledge.
Participate in talent selection.
Mentor less experienced software engineers and review their code, designs, and documentation.
Design, build, evaluate, and operate production AI agents on AWS, and expose customer context to other teams’ agents through secure, versioned APIs and governed agent tools, with guardrails, evaluations, observability, cost controls, and human review where customer impact requires it.
Lead the team’s AI-assisted delivery model from written specification through deployment, maintaining human approval gates and measuring success by quality and lead time.
Participate in an on-call rotation.
Remain compliant with company policies, processes, and legal guidelines; perform other duties as assigned and attend as required by the department.