Security AI / ML Engineer, Lead

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, ‘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 ML/AI Security Lead 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.

What you'll be doing

·       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 architecture

·       Experience building evaluation pipelines — automated metrics, human evaluation protocols, A/B testing

·       Understanding of transformer architectures and attention mechanisms (not just API calls)

·       Python fluency with production engineering practices (testing, CI/CD, monitoring)

·       Strong communication skills with the ability to explain model behavior and limitations to non-ML stakeholders

Added bonus if you have

·       Experience with security or cybersecurity data — alert classification, threat detection, anomaly detection

·       Familiarity with model serving at scale (vLLM, Triton Inference Server, Tensor RT optimization)

·       Hugging Face Ecosystem Experience Model Hub, tokenizers, datasets library, PEFT

·       Experience with RAG architecture and vector databases

·       Background in multi-model routing or mixture-of-experts approaches

·       Understanding of agentic AI patterns — tool use, chain-of-thought, multi-step reasoning

·       Experience with model cost optimization — quantization, distillation, caching strategies

·       Self-motivated individual who thrives in ambiguous environments and can build processes from the ground up

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