Lead ML/AI Engineer
Toyota USA2 days ago
Plano, TX, USAStaff+
Responsibilities
- Design, build, and maintain end-to-end ML pipelines covering data ingestion, feature engineering, 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 through Amazon Bedrock, custom training through SageMaker, classical ML, or hybrid approaches.
- Own ML features from design through deployment, including testing, observability, and post-launch monitoring.
- Contribute technical proposals, tradeoff analysis, and architecture decisions for ML systems and tooling.
- Debug training instability, data pipeline failures, inference latency, model drift, and other complex ML system issues.
- Write production-quality ML code, participate in code reviews, and mentor junior and mid-level engineers.
- Collaborate with Product, Data Science, Front-End Engineering, and Backend Engineering teams to deliver AI-powered features.
- Proactively address data quality gaps, model reliability issues, and ML-specific technical debt.
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.
- Knowledge of supervised and unsupervised learning, deep learning architectures, optimization, and evaluation methodologies.
- Hands-on experience with LLMs, prompt engineering, RAG pipelines, embedding models, vector databases, and agent frameworks such as LangChain or LlamaIndex.
- Experience with AWS AI/ML services and related infrastructure, including Amazon Bedrock, Amazon SageMaker, Lambda, Step Functions, S3, EventBridge, SQS, SNS, and OpenSearch or similar tools.
- Strong Python proficiency and experience with PyTorch, TensorFlow, or JAX, plus Hugging Face Transformers, scikit-learn, and XGBoost.
- Experience with MLOps practices, experiment tracking, model registries, and CI/CD for ML workflows.
- Experience with data engineering fundamentals including ETL pipelines, feature stores, data validation, and structured and unstructured data.
- Understanding of Infrastructure as Code using AWS CDK, CloudFormation, or Terraform.
- Experience monitoring ML systems, including model performance, data drift, and alerting.
- Preferred qualifications include a master’s degree, financial services experience, responsible AI expertise, NLP, real-time inference optimization, containerized ML workloads, computer vision, GraphQL or API gateway experience, AWS certifications, and open-source ML contributions.
Benefits
- Team-oriented, flexible, and respectful work environment.
- Professional growth and development programs and tuition reimbursement.
- Team Member Vehicle Purchase Discount and, if applicable, Toyota Team Member Lease Vehicle Program.
- Comprehensive healthcare and wellness plans for the employee’s family.
- Toyota 401(k) Savings Plan with company match and, if applicable, an annual retirement contribution regardless of employee contribution.
- Paid holidays and paid time off.
- Referral services for prenatal services, adoption, childcare, schools, and more.
- Health Savings Account, Health Care FSA, and Dependent Care FSA.
- Relocation assistance, if 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.