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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, TN, (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 Enterprise Platforms team is looking for a passionate and highly motivated Senior ML Platform Engineer. The primary responsibility of this role is to design, build, and operationalize an enterprise\-grade ML platform on AWS SageMaker Unified Studio. You will lead the organization’s migration from a fragmented ML toolchain to a unified, governed environment, directly impacting how we handle the full ML lifecycle—from initial data discovery to production deployment and monitoring. Reporting to the Enterprise Platforms leadership, the person in this role will support the team’s objective to scale our ML infrastructure and empower data teams to deliver high\-impact AI solutions with speed and reliability.
What you’ll be doing
In this role, you will be the architect of our ML ecosystem, ensuring that our platform is not only robust and scalable but also a seamless experience for our data scientists and engineers. Success means building a high\-performance, governed environment where production workloads run reliably and innovation is accelerated through standardized, automated workflows.
- Architect cloud\-native platform capabilities that power production ML workloads and support enterprise\-scale adoption
- Drive platform standardization by standing up SageMaker Unified Studio, including domain configuration, project provisioning, and persona\-based access
- Build and maintain automated MLOps pipelines that streamline data extraction, training, model registration, and deployment
- Govern the ML lifecycle through model versioning, lineage tracking, and cross\-account promotion using SageMaker Model Registry
- Enable reproducible experimentation by configuring MLflow for robust tracking of parameters, metrics, and artifacts
- Strengthen platform security by implementing identity and access controls with Okta SSO and SailPoint
- Deliver reliable real\-time and batch prediction workflows while proactively monitoring model performance, drift, and data quality
- Own platform observability and operational excellence through CloudWatch, Datadog, and root cause analysis
- Collaborate across technical and business teams to improve workflows, remove friction, and accelerate delivery of AI solutions
What you bring
- A bachelor’s degree in a relevant field that provides a strong foundation in software engineering, cloud platforms, or machine learning
- 7\+ years of software engineering experience in cloud infrastructure or ML platform operations, with experience navigating complex production environments
- 4\+ years of hands\-on AWS experience, including Amazon SageMaker Studio, Pipelines, Model Registry, Endpoints, and Feature Store
- 3\+ years of experience building and operating production MLOps pipelines, including training, versioning, deployment, and rollback strategies
- Proficiency with infrastructure\-as\-code tools such as Terraform, CDK, or CloudFormation to build repeatable, scalable environments
- Deep understanding of IAM design for ML, including execution roles, service roles, and cross\-account access management
- Strong collaboration and communication skills, with the ability to work independently while partnering effectively across teams
Added bonus if you have
- Advanced knowledge of SageMaker Unified Studio, including domain provisioning, custom blueprints, and project standardization
- Hands\-on experience with SageMaker Feature Store for online and offline feature management
- Experience using SageMaker Model Monitor for data quality checks, bias detection, and drift detection
- An AWS Machine Learning Specialty certification that demonstrates deeper technical expertise
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 Advantage 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. 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 3,708 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 $220,000 based on 47 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Toyota North America AI Hiring
Toyota North America has 2 open AI roles right now. They're hiring across MLOps Engineer, Data Scientist. Based in Plano, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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