Lead ML/AI Engineer

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, or Amazon SageMaker  for model training, hosting, and MLOps pipelines Lambda  and  Step Functions  for orchestrating inference workflows S3  for data storage and model artifact management EventBridge ,  SQS , or  SNS  for event-driven ML pipelines OpenSearch  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