About the role
Overview
Who we are
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, '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
At TFS, we're building next-generation products that redefine mobility for millions of customers worldwide. We're looking for a Lead Engineer in ML/AI - an individual contributor who combines strong machine learning fundamentals with the engineering discipline to ship production-grade intelligent systems on AWS.
You're past the point of running experiments in notebooks. You think end-to-end: from data and model design to deployment and monitoring, and you help the team move faster by raising the quality and reliability of every ML system you touch. You're starting to shape technical direction, not just execute it - and you're ready to be the go-to person on your team for applied AI.
What you'll be doing
Design, build, and maintain end-to-end ML pipelines - from data ingestion and feature engineering to model training, evaluation, deployment, and monitoring
Lead the integration of large language models into product features, including prompt engineering, retrieval-augmented generation (RAG), and agent-based patterns
Select and apply the right approach for each problem: foundation models via Amazon Bedrock, custom training on SageMaker, classical ML, or hybrid approaches
Take ownership of ML features from design through deployment - including testing, observability, and post-launch monitoring
Participate actively in technical design discussions, contributing well-reasoned proposals and tradeoff analysis across ML architecture and tooling choices
Debug and troubleshoot complex issues across ML systems - from training instabilities and data pipeline failures to inference latency and model drift
Write clean, production-quality ML code and hold a high bar in code reviews
Mentor junior and mid-level engineers through pairing, code reviews, and knowledge sharing on ML/AI topics
Collaborate with Product, Data Science, and Front-End/Backend Engineering teams to deliver AI-powered features
Identify and address data quality gaps, model reliability issues, and ML-specific tech debt proactively
What you bring
Bachelor's degree in Computer Science, Machine Learning, Statistics, or related field, or equivalent practical experience
5+ years of software engineering experience, including 2-4 years focused on ML/AI in production
Solid understanding of machine learning fundamentals: supervised and unsupervised learning, deep learning architectures (transformers, CNNs, RNNs), optimization techniques, and evaluation methodologies
Hands-on experience with large language models: prompt engineering, RAG pipelines, embedding models, vector databases, and agent frameworks (LangChain, LlamaIndex, or similar)
Experience working with AWS AI/ML services, including: Amazon Bedrock for foundation model access and knowledge bases, orAmazon SageMaker for model training, hosting, and MLOps pipelinesLambda and Step Functions for orchestrating inference workflowsS3 for data storage and model artifact managementEventBridge, SQS, or SNS for event-driven ML pipelinesOpenSearch or similar for vector search and semantic retrieval
Strong proficiency in Python - you write production-quality ML code, not just notebooks
Experience with core ML frameworks: PyTorch, TensorFlow, or JAX, and libraries like Hugging Face Transformers, scikit-learn, and XGBoost
Familiarity with MLOps practices: experiment tracking (MLflow, W&B), model registries, and CI/CD for ML workflows
Experience with data engineering fundamentals: ETL pipelines, feature stores, data validation, and working with structured and unstructured data
Understanding of Infrastructure as Code using AWS CDK, CloudFormation, or Terraform for ML infrastructure
Experience with observability and monitoring for ML systems: model performance tracking, data drift detection, and alerting
Clear communicator - comfortable discussing model tradeoffs and technical decisions with teammates and stakeholders
Added bonus if you have
Master's degree in Machine Learning, AI, Computer Science, Statistics, or related field
Experience in the financial services, banking, or insurance industry
Experience with responsible AI: fairness metrics, bias detection, explainability (SHAP, LIME), and model governance
Familiarity with NLP beyond LLMs (named entity recognition, document understanding, OCR)
Exposure to real-time inference optimization: quantization, distillation, ONNX, or latency-sensitive serving
Experience with containerized ML workloads (ECS Fargate, Docker) for training and serving
Exposure to computer vision or multi-modal architectures combining text, image, and structured data
Familiarity with GraphQL or API gateway patterns for exposing ML services to backend consumers
AWS certifications (Machine Learning Specialty, Developer Associate, or Cloud Practitioner)
Experience contributing to open-source ML projects
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 (if applicable)
• 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.
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 talent.acquisition@toyota.com.