Senior AI/ML Lead Engineer

Overview

Who we are

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.

Who we're looking for

At TFS, we're building next-generation products that redefine mobility for millions of customers worldwide. We're looking for a Sr Lead Engineer — an individual contributor at the principal level — who brings deep expertise in machine learning, AI systems, and large language models, combined with the engineering rigor to ship production-grade intelligent systems on AWS.

This isn't a management role. It's for the engineer who sees the signal in the noise: the one who can take a business problem, frame it as an ML challenge, build the model, deploy the pipeline, and make it reliable at scale. You'll shape our AI strategy from the ground up, work across teams to embed intelligence into our products, and mentor engineers who want to grow in this space. If you want to do meaningful applied AI work — not just research, not just wrappers around APIs — this is the role.

This position is based in Plano, TX. The selected candidate will be expected to reside within a commutable distance of this location.

Key Responsibilities

• Serve as the technical authority for ML/AI architecture across one or more product domains, making high-impact decisions on model selection, training strategies, inference patterns, and tooling
• Design, build, and maintain end-to-end ML pipelines — from data ingestion and feature engineering to model training, evaluation, deployment, and monitoring
• Lead the integration of large language models into production systems, including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and agent-based architectures
• Evaluate and select the right approach for each problem: foundation models via Amazon Bedrock, custom training on SageMaker, classical ML, or hybrid approaches
• Lead technical design reviews, architecture discussions, and RFC processes for AI/ML initiatives — driving alignment across engineering teams
• Identify and resolve systemic issues: model drift, data quality gaps, latency bottlenecks, cost inefficiencies, and scaling constraints in ML systems
• Define and champion engineering best practices for ML: experiment tracking, model versioning, reproducibility, testing strategies, and responsible AI principles
• Collaborate closely with Engineering Managers, Product, Data Science, and Front-End/Backend Engineering to shape roadmaps and ensure technical feasibility of AI-powered features
• Mentor and grow engineers at all levels through code reviews, pairing, design feedback, and technical guidance on ML/AI topics
• Contribute to hiring by conducting technical interviews and helping define what great looks like for ML/AI engineering at TFS
• Proactively communicate technical risks, tradeoffs, and recommendations to both engineering and non-technical stakeholders

What you bring

• Bachelor's degree in Computer Science, Machine Learning, Statistics, or related field, or equivalent practical experience
• 7+ years of software engineering experience, including 3–5 years focused specifically on ML/AI in production, with a track record of operating at a principal or staff engineer level
• Deep understanding of machine learning fundamentals: supervised and unsupervised learning, deep learning architectures (transformers, CNNs, RNNs), optimization techniques, and evaluation methodologies
• Hands-on experience with large language models: prompt engineering, fine-tuning (LoRA, QLoRA), RAG pipelines, embedding models, vector databases, and agent frameworks (LangChain, LlamaIndex, or similar)
• Production experience with AWS AI/ML services, including:
  • Amazon Bedrock for foundation model access, fine-tuning, and knowledge bases or
  • Amazon SageMaker for custom model training, hosting, and MLOps pipelines
  • Lambda and Step Functions for orchestrating inference workflows
  • S3 for data lakes and model artifact storage
  • EventBridge, SQS, or SNS for event-driven ML pipelines
  • OpenSearch or similar for vector search and semantic retrieval
• Strong proficiency in Python or Typescript — you write production-quality ML code, not just notebooks
• Experience with core ML frameworks: PyTorch, TensorFlow, or JAX, and libraries like Hugging Face Transformers, scikit-learn, and XGBoost
• Solid understanding of MLOps practices: experiment tracking (MLflow, W&B), model registries, CI/CD for ML, A/B testing, and canary deployments for models
• Experience with data engineering fundamentals: ETL pipelines, feature stores, data validation, and working with structured and unstructured data at scale
• Strong understanding of Infrastructure as Code using AWS CDK, CloudFormation, or Terraform for ML infrastructure
• Experience with observability and monitoring for ML systems: model performance tracking, data drift detection, and alerting
• Deep experience debugging complex issues across ML systems — from training instabilities to inference latency to data pipeline failures
• Strong written and verbal communication — you can write a clear RFC, lead a design review, and explain model tradeoffs to a non-technical stakeholder

Added bonus if you have

• Master's or PhD in Machine Learning, AI, Computer Science, Statistics, or related field
• Experience in the financial services, banking, or insurance industry
• Experience with responsible AI: fairness metrics, bias detection, explainability (SHAP, LIME), and model governance frameworks
• Familiarity with computer vision or NLP beyond LLMs (named entity recognition, document understanding, OCR)
• Experience with real-time inference at scale: model optimization (quantization, distillation, ONNX), GPU/accelerator management, and latency-sensitive serving
• Experience with multi-modal models and architectures that combine text, image, and structured data
• Hands-on experience with GraphQL federation or API gateway patterns for exposing ML services
• Experience with containerized ML workloads (ECS Fargate, Docker, Kubernetes) for training and serving
• AWS certifications (Machine Learning Specialty, Solutions Architect, Developer Associate)
• Published research or conference presentations in ML/AI
• Experience contributing to or maintaining open-source ML projects
• Experience defining engineering standards, writing ADRs, or leading org-wide technical initiatives

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
• 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)

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. Toyota is proud to have 10+ different Business Partnering Groups across 100 different North American chapter locations that support team members’ efforts to dream, do and grow without questioning that they belong.

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.