Software Engineering Manager II, Consumer 360

Credit Acceptance Corporation•San Francisco, CA

1d•Hybrid

About The Position

Credit Acceptance is investing in technology, data, AI, and continuous improvement to evolve its business. Engineering teams collaborate across the organization to solve complex challenges, build impactful solutions, modernize systems, and support informed business decisions. The company values curiosity, continuous learning, and ideas that drive meaningful business impact. The Software Engineering Manager II leads and mentors a cross-functional engineering team in delivering customer-focused products and business outcomes. The role supports agile planning and incremental delivery, designs solutions spanning multiple applications and teams, reinforces engineering standards, tracks delivery and technical-health metrics, and coaches team members. It also includes operational readiness, participation in on-call escalation and incident management, and ownership of shared API platforms and services. The manager drives AI adoption across the software development lifecycle and guides delivery of AI-enabled capabilities using appropriate evaluation, human oversight, observability, and responsible-use practices. This position follows a hybrid schedule: two work-from-home days and three standard 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 or equivalent experience.
At least 5 years of experience leading engineering teams.
Proven technical leadership, including mentorship, cross-functional project execution, and setting and executing technical vision and strategy.
Extensive experience implementing Agile and DevOps practices, including test-driven development, incremental releases, continuous integration, and deployment-pipeline automation.
Experience with microservices design and implementation, including migration planning, service granularity, interservice communication, traceability, orchestration, and failure isolation.
Experience with cloud migrations and mixed on-premises/cloud environments, including container orchestration, security, and serverless technologies.
Experience leading teams that design and operate reliable distributed services at scale, including performance, observability, failure recovery, and applying AI where it improves system capabilities or operations.
Experience creating reusable, developer-friendly platforms with self-service onboarding, API observability, and applied AI capabilities such as intelligent APIs, tool/function calling, and MCP-based integrations.
Experience building secure, scalable, cost-efficient cloud platforms, including deployment automation and applied AI workloads such as RAG, model services, agents, and AI-enabled engineering workflows.
Must reside in or be willing to relocate to the greater Southfield, Michigan area; greater Bellevue, Washington area; or San Francisco Bay Area, California.
Ability to communicate complex technical information verbally and in writing to all levels, including senior leadership.
Ability to solve problems at their source with simple, workable solutions and anticipate the impact of changes across multiple systems.
Ability to respond promptly and effectively to incidents, tasks, and projects.
Demonstrated ability and motivation to teach others, build trust, and develop relationships across the organization.
Ability to prioritize and execute tasks in a high-pressure environment.
Ability to distinguish durable engineering fundamentals from hype when evaluating emerging AI tools and patterns and to make pragmatic build-versus-buy and adopt-versus-wait decisions.
Required degrees must be from institutions accredited by the Council for Higher Education Accreditation or equivalent.

Nice To Haves

At least 4 years of experience leading initiatives in a Java ecosystem.
Track record of identifying opportunities to improve outcomes using new tools and approaches, evaluating and measuring candidate solutions, and advocating successfully for their adoption.
Experience applying suitable transaction and race-condition-resilient approaches, including database locking, distributed transactions (XA), and the SAGA pattern.
Ability to describe and design decoupled system transaction models resilient to upstream and downstream outages, with appropriate recovery models.
Experience implementing and operationalizing rules engines.
Experience modernizing or decomposing legacy monoliths into API-first, cloud-native services.
Experience introducing AI capabilities into an engineering organization, including creating an adoption plan, upskilling engineers, and measuring effects on delivery speed and quality.
Exposure to AI platform and tooling ecosystems, including model providers and gateways, agent frameworks, vector databases, and evaluation and observability tooling for LLM-based systems.

Responsibilities

Lead and mentor team members in agile and product principles, including early learning, self-organized and cross-functional teams, customer focus, outcome orientation, incremental delivery, and test-driven development.
Participate in work planning to align important work with the appropriate team and break it down for incremental delivery.
Lead and mentor cross-functional team members in building products that deliver customer delight and business value.
Design solutions for initiatives involving applications across multiple teams.
Support delivery teams in consistently following process and technology standards.
Develop and track delivery metrics, monitor product technical health, communicate progress toward delivery goals, and provide support when needed.
Build effective teams by ensuring the right people are in place and setting clear expectations.
Monitor team performance and continuously coach and mentor team members.
Prepare the team for operational success through understanding of the system ecosystem, architecture, technologies, domains, and their connection to business outcomes.
Participate in on-call escalation and incident management.
Own the design, delivery, and lifecycle governance of APIs and services used across teams, including contract-first design, versioning, deprecation, reuse, and consumer experience.
Drive adoption of AI across the software development lifecycle by establishing standards, guardrails, and measures of effectiveness for AI-assisted engineering.
Guide the identification, design, and delivery of AI-enabled capabilities, including agentic solutions, with appropriate evaluation, human-in-the-loop controls, observability, and responsible-use practices.