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About This Role
Overview:
When our square shaped burgers made their first sizzle on the scene more than 50 years ago, people knew our approach wasn’t like any other. Same goes for the way we support our employees. Our culture of openness, flexibility, and inclusiveness allows everybody to flourish in their own way. If you’re looking for a career where you can be part of the action as we continue to grow our iconic brand – We got you!
This Lead Engineer – AgenticAI Systems establishes the enterprise strategy, hardware roadmap, and system architecture for AI compute platforms, edge device deployments, and high-performance inference networks. This strategic role owns the reliability, cost optimization, security compliance, and long-term technical direction for all AI infrastructure powering store operations, drive-thru automation, and enterprise analytics. This role is to simplify and improve operations by leading the design of the infrastructure and platform that AI, machine learning, and agentic solutions run on. The primary focus is to lead the design and development of scalable model serving and MLOps/LLMOps systems, direct analysis to right-size and secure infrastructure, and build high quality AI systems that enhance business operations.
Another key element of the role is to contribute towards best practices and frameworks to deliver a scalable AI platform using newer tools and technology, while leading a cross-functional team and setting technical direction across multiple concurrent initiatives. Responsibilities:
- Define enterprise technical strategy, hardware roadmaps, and deployment architectures for distributed AI compute infrastructure, cloud platforms, and store-level edge fleets
- Establish enterprise system standards for reliability , disaster recovery, hardware security , and compute cost governance.
- Lead architectural spikes, capacity planning, and system benchmarking for next-generation AI workloads, multimodal models, and local edge inference.
- Partner with Enterprise IT, Security and cross function teams, and Hardware Vendors to design network topologies, edge hardware specifications, and secure API gateways
- Drive technical evaluations, vendor selection, and SLA negotiations for cloud infrastructure providers, GPU vendors, and edge hardware manufacturers
- Provide overall technical oversight, architectural review, and engineering mentorship across infrastructure and AI teams.
Minimum Wage: USD $122,000.00/Yr. Maximum Wage: USD $214,000.00/Yr. Qualifications:
- BS in Computer Engineering, Electrical Engineering, or Computer Science with 5+ years of systems engineering, cloud architecture, and infrastructure leadership.
- Master’s degree in computer science or a related field is preferred.
- Previous experience in the food service or related industry is preferred.
- Demonstrated success architecting multi-region cloud infrastructures, hybrid edge-cloud networks, and large-scale Kubernetes/GKE deployment fleets.
- Exceptional track record of directing complex engineering initiatives, setting technical vision, and negotiating multi-million-dollar technology investments.
- Mastery of heterogeneous compute topologies (GPU/NPU/TPU), low-latency networking, distributed storage, and inference acceleration runtimes.
- Proficient in the use of a variety of software applications including Python, Kubernetes/containerization tools, and cloud technologies (GCP, Azure, or AWS).
Expected Work Location (Remote): It is expected that you will primarily perform work remotely. You may be asked to travel, as needed, to the Wendy's Restaurant Support Center (RSC) (1 Dave Thomas Blvd, Dublin, OH 43017), or to other location(s) as designated by the Company. Changes to work location arrangements are subject to managerial approval and business needs. #LI-Remote
Wendy’s was built on the premise, "Quality is our Recipe®," which remains the guidepost of the Wendy's system. Today, Wendy's and its franchisees employ hundreds of thousands of people across more than 7,000 restaurants worldwide with a vision of becoming the world's most thriving and beloved restaurant brand.
The base pay range for this position is listed below. The base pay actually offered will take into account internal equity and budget for the open position and also may vary depending on the candidate’s job-related knowledge, skills, and experience, among other relevant factors. This range does not include an estimated value for any benefits, bonus, or other incentives that may be applicable based on position. The target annual bonus for this role is 20% of annualized base salary, based on actual company and personal performance.
Our square burgers make us different and so do our benefits! Our restaurant support roles are eligible for a wide array of benefits, including things such as parental leave, free EAP sessions, company 401k match and other great offerings. For more details about our benefits, including an overview of eligibility and terms for certain benefits, please visit our benefits website, www.wendysbenefits.com.
NOTE: Wendy’s benefits, bonus, and other incentives are governed by the applicable legal plans and policies and, where appropriate, may be subject to Board approval an individual award agreement terms. Those documents supersede all other information regarding Wendy’s benefits, bonus, and other incentives. Wendy’s retains the right to amend or terminate its plans and policies at its sole discretion, in accordance with applicable plans, policies and laws.
Salary Context
This $122K-$214K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Wendy's, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($168K) sits 21% below the category median. Disclosed range: $122K to $214K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Wendy's AI Hiring
Wendy's has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dublin, OH, US. Compensation range: $214K - $214K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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