Software Engineering Manager II, Consumer 360

Credit Acceptance Corporation•San Francisco, CA

6d•Hybrid

About The Position

Credit Acceptance is seeking a Software Engineering Manager II for its Consumer 360 organization. The role leads and mentors engineering teams, supports agile and product-minded delivery, and guides the design, delivery, and lifecycle governance of API platforms and services used across teams. The manager helps teams deliver customer-focused products and business value through incremental work, consistent engineering standards, delivery metrics, and attention to technical health and operational readiness. The role also leads technical direction across systems and teams, including distributed services, cloud platforms, and AI-enabled capabilities. It drives responsible AI adoption throughout the software development lifecycle, establishes standards and measures for AI-assisted engineering, and guides the design and evaluation of AI and agentic solutions. The position participates in on-call escalation and incident management and works in a hybrid model: two work-from-home days and three in-office days each week at a local office in one of the listed hub areas.

Requirements

Bachelor’s degree or equivalent experience.
At least 5 years of experience leading engineering teams.
Proven technical leadership experience, 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; the greater Bellevue, Washington area; or the San Francisco Bay Area, California.
Ability to influence and partner with business stakeholders to innovate and drive outcomes.
Ability to communicate complex technical information verbally and in writing to all organizational levels, including senior leadership.
Ability to solve problems at their source with simple, working solutions and anticipate the effects of changes across multiple systems.
Demonstrated ability and motivation to teach others and build trusted relationships throughout 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 finding opportunities to improve outcomes with new tools and approaches, evaluating and measuring potential solutions, and advocating successfully for adoption.
Experience applying appropriate transaction and race-condition-resilience 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 existing engineering organization, including developing an adoption plan, upskilling engineers, and measuring impact 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 tools for LLM-based systems.

Responsibilities

Lead and mentor team members in agile and product mindset practices, including early learning, self-organized teams, cross-functionality, customer focus, outcome orientation, incremental delivery, and test-driven development.
Participate in work planning to align important work with the appropriate team and break work down for incremental delivery.
Lead and mentor cross-functional team members in building products that deliver customer value and business outcomes.
Design solutions for initiatives involving applications owned by multiple teams.
Support delivery teams in consistently following process and technology standards.
Develop and track delivery metrics, monitor product technical health, report progress toward delivery goals, and provide support when needed.
Build effective teams by selecting appropriate team members and setting clear expectations.
Monitor team performance and continuously coach and mentor team members.
Maintain a sound understanding of the system ecosystem, architecture, technologies, and domains to support operational success and business outcomes.
Participate in on-call escalation and incident management.
Own the design, delivery, and lifecycle governance of shared API platforms and services, 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 evaluation, human-in-the-loop controls, observability, and responsible-use practices.
Lead technical direction and partner with business stakeholders to innovate and drive outcomes.
Communicate complex technical information to audiences at all levels, including senior leadership.
Prioritize and execute tasks in a high-pressure environment and respond effectively to incidents, tasks, and projects.
Remain compliant with company policies, processes, and legal guidelines; perform other duties as assigned and meet attendance requirements.