Lead ML/AI Engineer
Toyota USA1 day ago
Plano, TX, USAStaff+
Responsibilities
- Design, build, and maintain end-to-end ML pipelines from data ingestion and feature engineering through training, evaluation, deployment, and monitoring.
- Integrate large language models into product features using prompt engineering, retrieval-augmented generation, and agent-based patterns.
- Select and apply foundation models, custom training, classical ML, or hybrid approaches using AWS AI/ML services.
- Own ML features from design through deployment, including testing, observability, and post-launch monitoring.
- Contribute technical proposals and tradeoff analysis for ML architecture and tooling decisions.
- Debug training, data pipeline, inference latency, model drift, and other complex ML system issues.
- Write production-quality ML code, participate in code reviews, and address data quality, reliability, and ML technical debt.
- Mentor junior and mid-level engineers and collaborate with Product, Data Science, Front-End, and Backend Engineering teams.
Requirements
- Bachelor’s degree in Computer Science, Machine Learning, Statistics, or a related field, or equivalent practical experience.
- At least 5 years of software engineering experience, including 2–4 years focused on production ML/AI.
- Strong knowledge of supervised and unsupervised learning, deep learning architectures, optimization, and evaluation methodologies.
- Hands-on experience with large language models, prompt engineering, RAG pipelines, embedding models, vector databases, and agent frameworks.
- Experience with AWS AI/ML services, ML frameworks, MLOps, data engineering, infrastructure as code, and ML observability.
- Strong Python proficiency and experience writing production-quality ML code.
- Experience with PyTorch, TensorFlow, or JAX, plus libraries such as Hugging Face Transformers, scikit-learn, and XGBoost.
- Experience with ETL pipelines, feature stores, data validation, structured and unstructured data, model registries, experiment tracking, and CI/CD for ML workflows.
- Experience with AWS CDK, CloudFormation, or Terraform for ML infrastructure.
- Clear communication skills for discussing model tradeoffs and technical decisions.
- Preferred qualifications include a master’s degree, financial services experience, responsible AI expertise, NLP, real-time inference optimization, containerized ML workloads, computer vision or multimodal systems, GraphQL or API gateway experience, AWS certifications, and open-source ML contributions.
Benefits
- Teamwork-focused and flexible work environment with professional growth, development programs, and tuition reimbursement.
- Team Member Vehicle Purchase Discount and Toyota Team Member Lease Vehicle Program where applicable.
- Comprehensive health care and wellness plans for the employee’s family.
- Toyota 401(k) Savings Plan with company match and an annual retirement contribution where applicable.
- Paid holidays and paid time off.
- Referral services for prenatal services, adoption, childcare, schools, and related needs.
- Health Savings Account, Health Care FSA, and Dependent Care FSA tax-advantaged accounts.
- Relocation assistance where applicable.
Tech Stack
Categories
About Toyota USA
Toyota USA (Toyota Motor North America) manufactures and sells Toyota cars, trucks, and SUVs for U.S. customers, and provides financing and mobility services. Founded in 1957 and headquartered in Plano, Texas, it oversees North American operations and plants that build models like the Camry and Tundra, and is expanding battery and EV production in Kentucky and North Carolina.