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About This Role
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.
*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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Toyota North America, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Toyota North America AI Hiring
Toyota North America has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span McKinney, TX, US, Plano, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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