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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.
*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
Toyota Financial Services is looking for a passionate and highly motivated General Manager, Data Science \& Machine Learning. Reporting to the Vice President of Risk, this role will define, develop, deploy, and scale analytical, data science, machine learning, and application capabilities across TFS.
The General Manager leads a large enterprise data science and machine learning organization by setting technical direction, establishing standards for model development and deployment, and ensuring strong governance, compliance, and operational rigor. The position is responsible for delivering reliable, scalable analytical solutions that drive business value, partnering with business leaders to define decision\-support capabilities, and building a strong talent pipeline to advance the organization’s technical and leadership capabilities. In addition, this role works closely with business and technology executives to identify, prioritize, and deliver analytics and machine learning initiatives that create meaningful enterprise value. It translates complex business challenges into strategic roadmaps, investment priorities, and measurable delivery plans, while influencing decisions that shape how the enterprise allocates resources, manages risk, and pursues growth opportunities. The role also defines the long\-term strategy for data science and machine learning engineering capabilities, including talent, platforms, governance, and business engagement, and represents the organization in executive planning, budgeting, and governance discussions. The position collaborates across risk, audit, compliance, finance, and technology to manage model risk, operational risk, and regulatory exposure.
The person in this role also serves as a subject matter expert on technical requirements and data team needs and is accountable for key decisions across the organization. This includes determining which initiatives to advance based on customer input and partnership, making pricing and strategic decisions as a member of the VPP Working Group, deciding on model implementation as a member of ASOP, and helping establish governance standards for model development as a member of the Model Governance Council. The General Manager is also responsible for decisions related to the promotion of data scientists.
This position is based at our North American headquarters in Plano, Texas. The selected candidate will be expected to reside within commutable distance of this location.
What you’ll be doing
Leadership \& Team Management
- Lead a unified, 60\-person, multi\-level enterprise organization spanning data science and machine learning engineering, including senior leaders, managers, senior individual contributors, and technical teams.
- Define and lead a talent strategy for attracting, assessing, hiring, and retaining exceptional technical and leadership talent within the constraints of the enterprise.
- Develop learning programs for Data Science.
Enterprise Strategy \& Technical Direction
- Set enterprise standards and technical direction across modeling, experimentation, deployment, monitoring, and governance.
- Ensure analytical and machine learning systems are designed as reliable, auditable, end\-to\-end decision systems.
- Establish high standards for reproducibility, data quality, code quality, validation, release readiness, and production support.
- Oversee the full progression of work from problem framing and prototype development through production deployment, adoption, and continuous improvement.
Product, Platform \& Solution Delivery
- Guide the development of production\-grade solutions on modern cloud\-based platforms such as AWS and Snowflake.
- Lead delivery of a broad portfolio of analytical assets and applications, ranging from best\-in\-class predictive decisioning models to end\-to\-end business solutions with intuitive interfaces, configurable workflows, embedded analytics, reporting, and enterprise system integration.
- Product ownership responsibilities for Pricing.
Business Partnership \& Value Creation
- Partner with executives and business leaders to define decision\-support capabilities that improve business outcomes, customer experience, and operational effectiveness. These stakeholders can include risk, audit, compliance, finance, and technology to manage model risk, operational risk, and regulatory exposure.
Risk, Compliance \& Governance
- Ensure regulatory compliance through the development, deployment, and monitoring of analytical tools. Examples include Fair Lending monitoring, FDIC, and compliance with CECL and IFRS standards in TMCC’s critical accounting estimates.
What you bring
- Graduate degree in Data Science or a closely related field of study.
- Executive technical leadership: 15\+ years of relevant professional experience in data science, machine learning, or applied analytics, including substantial hands\-on ownership of analytical model development and production machine learning systems.
- Demonstrated success in applying predictive, prescriptive, forecasting, simulation, optimization, and related methods to complex business problems across multiple domains.
- Financial services and regulated environment experience: Significant experience in financial services, including work in regulated decisioning environments and model\-driven processes with governance, auditability, and financial or regulatory impact.
- People leadership: people\-management experience, including leadership of technical organizations, leadership of managers of managers, coaching senior leaders, and direct management of senior individual contributors. Proven ability to build high\-performing teams, strengthen leadership capability, and create environments in which technical talent thrives.
- Production machine learning lifecycle ownership: Demonstrated experience building, deploying, and operating machine learning or optimization systems in production, with accountability across the full lifecycle from design and development through deployment, monitoring, drift management, and retraining in the cloud.
- Programming, cloud, and data platform proficiency: Strong proficiency in Python and SQL, along with hands\-on experience with tools such as R or SAS, cloud platforms such as AWS, GCP, or Azure, and modern data technologies such as Snowflake, Spark, or Databricks.
- Executive presence and enterprise influence: Proven ability to shape strategy, lead cross\-functional prioritization, and translate complex analytical concepts and technical tradeoffs into clear recommendations for executives and senior business leaders.
- Governance mindset: Strong instinct for ensuring that analytical decisions can be demonstrated to be correct, reproducible, explainable, and defensible before deployment in production.
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 [email protected].
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Toyota North America AI Hiring
Toyota North America has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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