Security ML / AI Engineer, Lead

ML/AI Engineer, Security Intelligence Location: Plano, Texas To save time applying, Toyota does not offer sponsorship of job applicants for employment-based visas or any other work authorization for this position at this time. 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 architectures 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, TensorRT optimization) HuggingFace ecosystem experience — model hub, tokenizers, datasets library, PEFT Experience with RAG architectures 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 A work environment built on teamwork, flexibility, and respect Professional growth and development programs to help advance your career, as well as tuition reimbursement Vehicle purchase & lease programs 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) 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. #J-18808-Ljbffr Dormont Manufacturing Co