Sr. Machine Learning Engineer

Overview

Who we are

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 were looking for:

Toyotas 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 departments objective to deliver trusted scalable governed and actionable machine learning (ML) and analytics capabilities that improve data-driven decision-making across the organization.

Youll 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 youll 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 applicationswith graceful degradation clear fallback strategies andthe 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: Bachelors 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

• Masters 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 well 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

Belonging at Toyota

Our success begins and ends with our people. We embrace all perspectives and value unique human experiences. Respect for all is our North Star. Toyota is proud to have 10 different Business Partnering Groups across 100 different North American chapter locations that support team members efforts to dream do and grow without questioning that they belong.

Applicants for our positions are considered without regard to race ethnicity national origin sex sexual orientation gender identity or expression age disability religion military or veteran status or any other characteristics protected by law.

Have a question need assistance with your application or do you require any special accommodations Please send an email to .