Overview

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.

What you'll be doing

• 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.
• Operationalize and innovate with models and analytical logic: Develop model-inference services, optimization methods, and decision logic into reliable production workflows and applications, while identifying new ways to improve speed, quality, automation, and business impact through innovative solutions.
• 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
• Strengthen testing, reliability, and observability: Build regression, golden-dataset, and reproducibility tests that protect decision quality before changes reach production. Monitor for calibration drift, prediction and input drift, segment-level degradation, and training-to-production skew, and partner across teams to implement logging, alerting, runbooks, documentation, and release controls.
• 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 Science, Statistics, Mathematics, or a related technical field, or equivalent practical experience
• Software engineering and production ML experience: 2+ years of hands-on experience building software in Python and SQL and specifically contributing to deploying or supporting production machine learning (ML) models, optimization engines, batch scoring pipelines, or model driven decision-support applications, with real fluency in version control, software design, testing, documentation, and code review practices and core data structures and algorithms.
• Cloud and data platform skills: Experience building or supporting cloud-based ML or analytics workflows, ideally on AWS and modern data platforms such as Snowflake, SageMaker, or equivalents.
• ML lifecycle fluency: Practical experience across the full ML lifecycle, including training, tuning, evaluation, deployment, monitoring, and retraining, across techniques such as regression, classification, optimization, or recommendation.
• Reliability and observability experience: Experience designing maintainable systems that fail loudly and using observability to detect issues and improve systems over time.
• Communication and judgment: Ability to translate technical trade-offs into business terms, write clear and practical design artifacts and exercise good judgment under ambiguity.

Added bonus if you have

• Master's or higher in a quantitative or technical discipline (CS, Engineering, Data Science, Statistics, Mathematics, Operations Research, etc.)
• Domain experience in regulated decisioning (lending, insurance, fraud, risk, pricing) and the governance and auditability practices that come with it
• Advanced MLOps experience: CI/CD, model registries, containerization (Docker, Kubernetes), infrastructure-as-code, automated drift detection, data validation, or deployment governance
• Application and API integration experience exposing model outputs, decision logic, or optimization results to downstream systems using multiple integration patterns, such as synchronous APIs, asynchronous workflows, event-driven architectures, or batch interfaces.
• Generative AI application experience: LLM-powered workflows, RAG, semantic search, evaluation, guardrails, monitoring, or responsible-AI practices
• Relevant credentials: AWS Certified Machine Learning Engineer - Associate, Solutions Architect, Developer, or equivalent

What we'll bring

During your interview process, our team can fill you in on all the details of our industry-leading benefits and career development opportunities. A few highlights include:

• A work environment built on teamwork, flexibility and respect
• Professional growth and development programs to help advance your career, as well as tuition reimbursement
• Team Member Vehicle Purchase Discount
• Toyota Team Member Lease Vehicle Program (if applicable)
• Comprehensive health care and wellness plans for your entire family
• Toyota 401(k) Savings Plan featuring a company match, as well as an annual retirement contribution from Toyota regardless of whether you contribute
• Paid holidays and paid time off
• Referral services related to prenatal services, adoption, childcare, schools and more
• Tax Advantaged Accounts (Health Savings Account, Health Care FSA, Dependent Care FSA)

Relocation assistance (if applicable