Sr. Machine Learning Engineer

About the role

Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world's most admired brands, Toyota is growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We're looking for talented team members who want to Dream. Do. Grow. with us. An important part of the Toyota family is Toyota Financial Services (TFS), the finance and insurance brand for Toyota and Lexus in North America. While TFS is a separate business entity, it is an essential part of this world-changing company- delivering on Toyota's vision to move people beyond what's possible. At TFS, you will help create best-in-class customer experience in an innovative, collaborative environment. Toyota does not offer support or sponsorship of job applicants for employment-based visas or any other work authorization for this role now or in the future. You must have the right to work in the United States and not require Toyota support or sponsorship for immigration-related employment (e.g., H-1B, O-1, E-3, H-1B1, TN, F-1 OPT, F-1 STEM OPT, F-1 CPT, TN, 'job flexibility benefits' (also known as I-140 or Adjustment of Status portability), etc. now or in the future. You should not apply for this role if you will require Toyota to assist with immigration support or sponsorship now or in the future. Who we're looking for: Toyota's Data Science department is looking for a passionate and highly motivated Machine Learning Engineer. The primary responsibility of this role is to operationalize complex models, analytical engines, optimization logic, and innovative decision-support applications, making them production-grade, tested, observable, and trustworthy. These systems must also be designed to be understood, maintained, and safely changed over time with their impact measured in business terms . Reporting to the National Manager, Data Science, the person in this role will support the department's objective to deliver trusted, scalable, governed, and actionable machine learning (ML) and analytics capabilities that improve data-driven decision-making across the organization. You'll work across multiple problem domains, building and owning end-to-end decision systems from model output to business action, with broad exposure across the organization and opportunities to deepen technical expertise. We are looking for a candidate who holds a high bar for technical quality, improves continuously, and moves quickly to solve complex problems with practical, elegant solutions. The ideal candidate uses data to guide design decisions, works well across data science, technology, and business teams, and communicates clearly with both technical and non-technical audiences.

Responsibilities

• Understand business problems and shape solution design: Partner with business stakeholders, data scientists, and technology leads to clarify needs, evaluate trade-offs, and influence practical designs that connect ML outputs to business outcomes.
• Build analytical engines and decision-support applications: Create solutions that combine model outputs, business rules, and optimization results into actionable recommendations and prescriptive decisions.
• Design and operate cloud-based analytical services: Use established AWS patterns to build solutions that are scalable, reliable, secure, observable, maintainable, and cost-conscious.
• Develop batch and real-time decisioning workflows: Build resilient applications with graceful degradation, clear fallback strategies, and the data capture needed to measure, learn from, and continually optimize decisions over time
• Apply emerging ML and Generative AI capabilities where they create value: Integrate model-driven recommendations, decision engines, LLM-powered workflows, retrieval-based systems, and other practical innovations that make data science outputs easier to consume and act on.
• Contribute to engineering best practices across teams: Support horizontal impact through code reviews, reusable components, CI/CD improvements, documentation, testing patterns, and production-readiness practices.
• What you bring
• Education: Bachelor's degree in Computer Science, Engineering, Data Sc

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