Lead ML/AI Engineer

Overview Who we are Collaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Toyota. As one of the world’s most admired brands, Toyota is growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We’re looking for talented team members who want to Dream. Do. Grow. with us.

An important part of the Toyota family is Toyota Financial Services (TFS), the finance and insurance brand for Toyota and Lexus in North America. While TFS is a separate business entity, it is an essential part of this world-changing company- delivering on Toyota's vision to move people beyond what's possible. At TFS, you will help create best-in-class customer experience in an innovative, collaborative environment.

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 At TFS, we're building next-generation products that redefine mobility for millions of customers worldwide. We're looking for a

Lead Engineer in ML/AI —

an individual contributor who combines strong machine learning fundamentals with the engineering discipline to ship production-grade intelligent systems on AWS.

You're past the point of running experiments in notebooks. You think end-to-end: from data and model design to deployment and monitoring, and you help the team move faster by raising the quality and reliability of every ML system you touch. You're starting to shape technical direction, not just execute it — and you're ready to be the go-to person on your team for applied AI.

What you'll be doing

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 product features, including prompt engineering, retrieval-augmented generation (RAG), and agent-based patterns

Select and apply the right approach for each problem: foundation models via

Amazon Bedrock , custom training on

SageMaker , classical ML, or hybrid approaches

Take ownership of ML features from design through deployment — including testing, observability, and post-launch monitoring

Participate actively in technical design discussions, contributing well-reasoned proposals and tradeoff analysis across ML architecture and tooling choices

Debug and troubleshoot complex issues across ML systems — from training instabilities and data pipeline failures to inference latency and model drift

Write clean, production-quality ML code and hold a high bar in code reviews

Mentor junior and mid-level engineers through pairing, code reviews, and knowledge sharing on ML/AI topics

Collaborate with Product, Data Science, and Front-End/Backend Engineering teams to deliver AI-powered features

Identify and address data quality gaps, model reliability issues, and ML-specific tech debt proactively

What you bring

Bachelor's degree in Computer Science, Machine Learning, Statistics, or related field, or equivalent practical experience

5+ years of software engineering experience, including 2–4 years focused on ML/AI in production

Solid 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, RAG pipelines, embedding models, vector databases, and agent frameworks (LangChain, LlamaIndex, or similar)

Experience working with

AWS AI/ML services , including:

Amazon Bedrock

for foundation model access and knowledge bases, or Amazon SageMaker

for model training, hosting, and MLOps pipelines Lambda

and

Step Functions

for orchestrating inference workflows S3

for data storage and model artifact management EventBridge ,

SQS , or

SNS

for event-driven ML pipelines OpenSearch

or similar for vector search and semantic retrieval

Strong proficiency in

Python

— 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

Familiarity with

MLOps practices : experiment tracking (MLflow, W&B), model registries, and CI/CD for ML workflows

Experience with

data engineering

fundamentals: ETL pipelines, feature stores, data validation, and working with structured and unstructured data

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

Clear communicator — comfortable discussing model tradeoffs and technical decisions with teammates and stakeholders

Added bonus if you have

Master's degree 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

Familiarity with

NLP

beyond LLMs (named entity recognition, document understanding, OCR)

Exposure to

real-time inference

optimization: quantization, distillation, ONNX, or latency-sensitive serving

Experience with

containerized ML workloads

(ECS Fargate, Docker) for training and serving

Exposure to

computer vision

or

multi-modal

architectures combining text, image, and structured data

Familiarity with

GraphQL

or API gateway patterns for exposing ML services to backend consumers

AWS certifications (Machine Learning Specialty, Developer Associate, or Cloud Practitioner)

Experience contributing to open-source ML projects

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.

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