• Design machine learning solutions and tools across the full ML lifecycle • Contribute to all areas of our data platform • Work closely with other machine learning engineers and data engineers

Role Description We are building the next generation of AI-driven game experiences, running generative models on-device, right where the players are — on phones, tablets, laptops, and desktops. As a Senior Machine Learning Engineer for On-Device & Mobile AI, you will take state-of-the-art multi-modal models and make them run fast, small, and reliably on mobile and constrained hardware. This is a deeply hands-on role. You will own the optimization and deployment of significant parts of the inference stack, shaping the latency, quality, memory footprint, and battery profile of AI features experienced by billions of players. What you'll be doing - Inference & On-Device Optimization: - Own the optimization pipeline for the models you ship: model export, graph transformation, operator fusion, memory-layout planning, and hardware-specific tuning. - Apply quantization (INT4/INT8/FP16), weight sharing, structured/unstructured pruning, and knowledge distillation. - Do low-level performance work: write and tune WebGPU compute shaders and native kernels; profile with browser and platform tools. - Apply efficiency techniques as engineering levers to meet budgets on target SKUs. - Runtime & Systems Integration: - Work with WebGPU-targeted inference runtimes and extend or build glue code where necessary. - Build parts of the integration between the ML runtime and the game engine. - Build supporting engineering for your components: model packaging, on-device fallbacks, crash/quality telemetry, and automated on-device benchmarking. - Research Productionization: - Partner with research scientists to turn novel CV and multi-modal architectures into deployable implementations. - Provide a feedback loop into research: surface hardware constraints and op-support gaps early. - Track breakthroughs in efficient inference and assess them pragmatically. - Collaboration & Engineering Quality: - Contribute to engineering best practices, code-review standards, and performance-regression gates. - Support a culture of measurement: track KPIs for latency, quality, memory, and power. - Partner with platform engineers, product managers, and runtime teams. - Share knowledge and mentor junior and mid-level engineers. Qualifications - 5+ years in software/ML engineering, with meaningful time focused on on-device / edge inference or real-time, performance-critical systems. - Production deployment of transformer- and/or diffusion-based models on mobile, desktop, or embedded hardware. - Hands-on experience with at least one major inference runtime and a working understanding of operator fusion, memory layout, and runtime scheduling. - Low-level performance engineering: solid command of at least one GPU/compute API and the profiling tools to go with it. - Working knowledge of model-optimization techniques and the judgment to apply them effectively. - Understanding of target hardware: mobile SoCs and/or desktop/laptop GPUs. - Strong Python for export pipelines and training-side tooling; familiarity with core languages of a browser-native runtime is a plus. - Working fluency with the models you deploy. - A collaborative working style: clear communication, reliable delivery, and a willingness to support and learn from teammates. Requirements - Experience shipping world-model, neural-rendering, or real-time generative pipelines on device. - Hands-on experience deploying models through WebGPU. - Game-engine or real-time-graphics background. - Contributions to open-source ML inference frameworks or GPU/compute libraries. - Familiarity with compiler stacks for custom kernel generation and graph optimization. - Experience with on-device benchmarking infrastructure and performance-regression CI. - Proficiency in C++/Objective-C/Swift for runtime integration. Benefits - Comprehensive health, life, and disability insurance. - Commute subsidy. - Employee stock ownership. - Competitive retirement/pension plans. - Generous vacation and personal days. - Support for new parents through leave and family-care programs. - Office food snacks. - Mental Health and Wellbeing programs and support. - Employee Resource Groups. - Global Employee Assistance Program. - Training and development programs. - Volunteering and donation matching program.

Role Description We are seeking a highly motivated and experienced Leader of ML and AI Engineering within the AI team. The ideal candidate will have a strong technical background in decision science, machine learning, and generative AI with a proven track record in solving business problems and implementing large-scale automated solutions in partnership with the respective engineering teams. The leader will partner with business and engineering stakeholders to formulate the vision to achieve the company’s strategic goals and co-lead the roadmap to deliver innovative solutions for dealers, consumers, and team members. As a Senior Manager, MLE at Credit Acceptance, you will play a pivotal role in the success of this mission as you will lead the development of AI-powered solutions across different business areas. This involves understanding the business processes, identifying new opportunities to add value using ML/AI algorithms, and harnessing data sources to build state-of-the-art ML/AI solutions. This position will work from home; occasional planned travel to an assigned Southfield, Michigan office location may be required. However, this position is permitted to work at a Southfield, Michigan office location if requested by the team member. - Lead the vision and the strategic execution with a strong focus on continuous and long-term value creation across all participants of our flywheel. - Collaborate with management and stakeholders to define strategic roadmaps and translate them into actionable quarterly plans. - Drive execution and delivery of ML/AI solutions by managing priorities, deadlines, and deliverables, leveraging your technical expertise. - Design and deliver scalable, secure systems using state-of-the-art AI/ML technologies and industry best practices, and nurture the culture of creating high-quality, well-tested systems to address critical product and business needs. - Troubleshoot and resolve complex technical issues to improve system reliability, scalability, and operational efficiency. - Ensure the security, scalability, and architectural integrity of feature designs through reviews across teams. - Deliver hands-on solutions while mentoring other data professionals (including MLEs) within the organization. - Explore and apply advanced machine learning techniques, including large language models (LLMs), deep learning, and graph neural networks, to solve complex challenges across the organization. - Guide a team of MLEs across different areas: - Mentoring: Mentor team members on design principles, coding standards, and the adoption of AI productivity tools. - Recommendations: Personalize guidance across different surfaces using deep learning methods; personalize layouts with Bayesian contextual multi-armed bandits. - Growth: Foster long-term growth through data-driven causality and incrementality. - Gen-AI: Power existing applications with Gen AI models and engineering to improve downstream experience and decisions. - Lifecycle: Using ML models (such as XGBoost & Causal Meta-Learner-based model, etc.), proactively guide business teams across different areas. - Engineering: With engineering partners, build ML and Gen-AI platform and inference pipelines for different types of models. Qualifications - PhD in Computer Science, Stats, Economics, or a relevant technical field with at least 8+ years of relevant experience or MS with at least 10+ years of experience in machine learning and software engineering. - 8+ years of hands-on experience designing, building and deploying AI (ML, DL, Gen-AI) models, including Reinforcement Learning algorithms, Recommendation systems, Transformers, fine-tuned LLMs, Regressions, etc., with a solid understanding of mathematics, statistics, and engineering needed to build such infrastructure. - Hands-on expertise in scaling and maintaining production-grade ML services, with a strong focus on ML/LLM Operations (versioning, automation, observability, automated training and monitoring, etc.) and ability to balance ML model complexity with production requirements. - Passion for identifying new business opportunities and experience of using a test and learn approach to bring scalable and efficient solutions integrating AI algorithms, ML/LLM Ops, and s/w engineering. - Experience partnering with the engineering, product, business operations, legal and other teams while designing, building, and executing solutions. - Strong problem-solving skills with bias for action. Requirements - Experience in automotive industry, especially in building ML/AI systems while ensuring local and central regulations. - Experience in model interpretability and responsible AI practices. - Expertise in data science, advanced experimentation and visualization techniques. - Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray). - Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints. - Experience with Databricks MLflow for ML lifecycle management and model versioning. - Hands-on experience with Databricks Model Serving for production ML deployments. - Proficiency with GenAI frameworks/tools and technologies such as Apache Airflow, Spark, Flink, Kafka/Kinesis, Snowflake, and Databricks. - Demonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models. - Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies. Benefits - Excellent benefits package that includes 401(K) match, adoption assistance, parental leave, tuition reimbursement, comprehensive medical/dental/vision and many nonstandard benefits that make us a Great Place to Work. Company Description Credit Acceptance is proud to be an award-winning company recognized both locally and nationally across multiple workplace categories. Our world-class culture is shaped by dedicated team members who are driven to succeed as professionals individually and together as a team. Backed by a strong product, exceptional people, and a stable financial foundation, we’ve grown into a leading provider of used and new car financing across the country.

• Define and execute the strategic roadmap for predictive modeling and machine learning • Lead cross-functional initiatives integrating analytics into business processes • Oversee development, deployment, and monitoring of high-impact models • Serve as a strategic advisor to senior leadership on data strategy and innovation • Represent Predictive Analytics in regulatory, audit, and governance forums • Foster partnerships across the organization