Lead ML/AI Engineer

Toyota•Plano, TX

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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

Benefits

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)