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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
Toyota Financial Services Enterprise Platforms team is looking for a passionate and highly motivated Lead AI/ML Platform Engineer. The primary responsibility of this role is to design, build, and implement scalable platform solutions that power enterprise AI/ML and GenAI capabilities across the organization. You will help enable secure, production\-ready MLOps and LLMOps infrastructure that supports model training, inference, orchestration, and retrieval\-augmented generation. The Lead AI/ML Platform Engineer will support the Enterprise Platforms team’s objective to deliver reliable, secure, and high\-performing AI platform capabilities that drive business value at scale.
What you’ll be doing
In this role, you’ll help shape the foundation for Toyota Financial Services’ next generation of AI platform capabilities, where success means building systems that are scalable, resilient, and ready for production use. A typical day may include collaborating with product, architecture, engineering, data, and cybersecurity partners to solve complex infrastructure challenges while improving the developer and model lifecycle experience.
- Design and implement cloud\-native infrastructure that enables enterprise AI/ML and GenAI workloads in production
- Build and evolve MLOps and LLMOps platform capabilities, including model training, versioning, deployment, monitoring, and rollback
- Create GPU\-accelerated compute environments that improve model performance while balancing scalability and cost efficiency
- Standardize infrastructure patterns for vector databases, model registries, and orchestration frameworks
- Develop reusable approaches for model serving, inference scaling, prompt management, and latency optimization
- Design secure, multi\-tenant environments with strong access controls, auditability, and usage governance for AI models
- Partner closely with engineering, platform, and data teams to ensure smooth data flow, strong observability, and operational resiliency
- Own technical direction for AI infrastructure services and integrations in collaboration with the architecture team
- Lead design reviews, establish engineering standards, and help guide critical technical decisions
- Mentor engineers, provide thoughtful feedback, and support growth through coaching and development planning
- Stay current on emerging GenAI, distributed systems, and infrastructure trends to bring fresh ideas and better solutions to the team
What you bring
- 10\+ years of experience in software engineering, with a focus on cloud infrastructure or cloud platform engineering
- 3\+ years of experience building cloud infrastructure that supports AI/ML workloads such as training, tuning, and inference
- Deep hands\-on experience with AWS and infrastructure\-as\-code tools such as Terraform, CDK, or CloudFormation
- Experience with Kubernetes, containerization, and CI/CD pipelines in a production environment
- Strong understanding of GPU infrastructure, serverless compute, and scalable microservice patterns
- Familiarity with model hosting, inference scaling, and observability tools such as Datadog, CloudWatch, or Prometheus
- Practical experience using Git/GitHub and CI/CD tooling such as GitHub Actions or Jenkins
Added bonus if you have
- Experience with AWS AI/ML services such as SageMaker or Bedrock
- Familiarity with LLMOps tooling and GenAI infrastructure such as LangChain or RAG pipelines
- Experience working with vector databases, model registries, or orchestration tools such as MLflow, Airflow, or Ray
- Knowledge of prompt management, token usage optimization, and model performance tuning
- AWS Solutions Architect Professional or Machine Learning certification
What we’ll bring
During your interview process, we’ll share details about our industry\-leading benefits and career development opportunities designed to support your growth and well\-being:
- A collaborative work environment built on teamwork, flexibility, and respect
- Professional growth programs including tuition reimbursement to advance your career
- Team Member Vehicle Purchase Discount and Lease Vehicle Program (if applicable)
- Comprehensive health care and wellness plans for you and your family
- Toyota 401(k) Savings Plan with company match plus annual retirement contributions regardless of your participation
- Paid holidays and paid time off for work\-life balance
- Referral services for prenatal care, adoption, childcare, schooling, and more
- Tax\-advantaged accounts including Health Savings Account (HSA), Health Care FSA, and 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 [email protected].
Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 3,824 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At Toyota North America, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $217,200 based on 76 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 $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Toyota North America AI Hiring
Toyota North America has 2 open AI roles right now. They're hiring across MLOps Engineer, AI/ML Engineer. Based in Plano, TX, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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.
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