Sr.Security ML / AI Engineer

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 Toyota Financial Services (TFS) Technology team is looking for a highly motivated person to fill a role as an Sr. ML/AI Security Engineer within the Security Intelligence Engineering organization. You'll own the intelligence layer of a new AI-powered security platform — starting with prompt engineering and managed AI service integration, then progressing to fine-tuning models on enterprise security data, and building a multi-model serving and routing layer. This role is what makes the organization own its intelligence rather than renting it from a vendor. You'll train models that understand the specific security environment, build the feedback loops that make them better over time, and ensure the AI layer delivers high accuracy on alert triage while keeping costs predictable through intelligent model routing.

Design and implement prompt engineering patterns for managed AI service integration Build training data pipelines from the security data lake — curating, labeling, and versioning datasets from real enterprise security telemetry Fine-tune models on organization-specific security data — alert triage, risk scoring, finding classification Implement the analyst feedback loop — capturing human corrections to continuously improve model accuracy Build model evaluation frameworks with rigorous metrics (F1, precision, recall, false positive rates) benchmarked against analyst agreement Design and implement a model routing layer — directing each task to the optimal model based on complexity, latency requirements, and cost Monitor models in production for drift, accuracy degradation, and emerging failure modes Implement centralized token usage monitoring for leadership visibility into AI consumption and cost control Collaborate with the Lead Engineer on agent architectures — multi-agent orchestration, tool use, and autonomous triage workflows Deploy and manage model inference endpoints across cloud ML services and container-based serving Build the analyst feedback loop: approval/rejection signals in dashboards feeding back into retraining pipelines What You Bring 3+ years in applied ML/AI engineering (not research-only — production deployment required) Hands-on experience with LLM fine-tuning — LoRA, QLoRA, or full fine-tuning on domain-specific data Experience with cloud ML platforms (e.g., AWS SageMaker): training jobs, hyperparameter tuning, model registry, endpoint deployment PyTorch proficiency for model training and custom architectures Experience