Interested in this AI/ML Engineer role at Restaurant Brands International?
Apply Now →Skills & Technologies
About This Role
Ready to make your next big professional move? Join us on our journey to achieve our big dream of building the most loved restaurant brands in the world.
Restaurant Brands International Inc. is one of the world's largest quick service restaurant companies with nearly $45 billion in annual system\-wide sales and over 32,000 restaurants in more than 120 countries and territories.
RBI owns four of the world's most prominent and iconic quick service restaurant brands – TIM HORTONS®, BURGER KING®, POPEYES®, and FIREHOUSE SUBS®. These independently operated brands have been serving their respective guests, franchisees and communities for decades. Through its Restaurant Brands for Good framework, RBI is improving sustainable outcomes related to its food, the planet, and people and communities.
RBI is committed to growing the TIM HORTONS®, BURGER KING®, POPEYES® and FIREHOUSE SUBS® brands by leveraging their respective core values, employee and franchisee relationships, and long track records of community support. Each brand benefits from the global scale and shared best practices that come from ownership by Restaurant Brands International Inc.
We are seeking a highly motivated and experienced Senior Engineer, AI Enablement to join our team. In this role, you will lead the design, development, and implementation of scalable AI solutions that support a broad array of AI initiatives across RBI and its brands. You will partner closely with business stakeholders, product teams, data engineers, and analytics leaders to solve complex business challenges.
This role requires strong technical expertise, strategic thinking, and the ability to drive projects from concept through production deployment while mentoring junior team members and influencing technical direction. You will be expected to stay at the forefront of AI advancements, continuously evaluating emerging technologies and translating new AI capabilities into practical, high\-impact solutions that enhance and expand RBI's ecosystem.
RBI follows a 5 day, in\-office work schedule to support collaboration. Candidates should be comfortable working onsite 5 days per week out of our office in Miami, FL or Toronto, Ontario.
What You'll Do
- Lead the end\-to\-end development of AI solutions that directly serve our franchisees, including complex conversational interfaces, autonomous agents, and intelligent automation systems. You will architect systems that handle real\-world ambiguity, integrate with brand data, and operate reliably at the scale of Tim Hortons, Burger King, Popeyes, and Firehouse Subs.
- Build and deploy production\-grade autonomous agents capable of executing multi\-step workflows, reasoning over brand and operational data, and acting on behalf of franchisees and corporate users. Our team designs and builds chatbot experiences that go beyond simple Q\&A to deliver genuine business value.
- Partner with brand and data teams to identify automation opportunities, scope solutions, and ship working software quickly. Translate ambiguous business problems into well\-architected technical solutions and move them from prototype to production with appropriate engineering rigor.
- Mentor junior engineers, contribute to engineering standards and best practices, and help shape the technical direction of the AI Enablement function.
What You'll Bring
- Proven experience building production AI applications, including LLM\-powered chatbots, retrieval\-augmented systems, and autonomous agents
- Hands\-on experience with modern AI development frameworks and agent orchestration patterns
- Strong agentic coding fluency, using AI coding assistants to accelerate development without sacrificing code quality
- Ability to evaluate model outputs, design evaluation harnesses, and iterate on prompt and system design based on real performance data
Software Engineering Fundamentals
- Strong programming skills in Python and at least one other modern language such as TypeScript.
- Experience designing and shipping production services, including API design, data modeling, and asynchronous processing
- Deep understanding of secure coding practices, including input validation, secrets management, authentication and authorization patterns
- Familiarity with handling sensitive data, including PII, payment data, and franchisee operational data
DevOps and Infrastructure
- Strong proficiency in Infrastructure as Code with Terraform, including module design, state management, and multi\-environment deployments
- Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions, GitLab CI, or equivalent
- Working knowledge of AWS, including IAM, networking, compute, and managed AI services
- Container and orchestration experience with Docker and Kubernetes or equivalent managed services
- Observability mindset, with experience instrumenting systems for logging, metrics, and tracing using tools such as Datadog
Data and Integration
- Comfort working with modern data platforms, including Snowflake, and understanding of how AI systems consume and produce structured and unstructured data
- Experience integrating with enterprise systems, APIs, and third\-party platforms
- Familiarity with vector databases, embeddings, and retrieval patterns
Collaboration and Leadership
- Track record of partnering with non\-technical stakeholders to scope and deliver high\-impact work
- Strong written and verbal communication skills, with the ability to explain technical tradeoffs to brand, product, and executive audiences
- Experience mentoring engineers and raising the technical bar of a team
- Bias toward shipping, balanced with sound judgment about when to invest in quality, security, and scalability
What Sets You Apart
- Experience in retail, restaurant, or franchise environments
- Familiarity with the Anthropic API, Claude, MCP, or comparable enterprise AI platforms
- Experience deploying AI solutions to non\-technical end users at scale
- Background in security engineering or building systems subject to enterprise security review
- Experience working with Snowflake and modern cloud data platforms preferred.
- Familiarity with containerization and orchestration technologies such as Docker and Kubernetes preferred.
- Strong analytical, problem\-solving, and systems\-thinking capabilities.
- Demonstrated ability to lead projects and influence technical direction across cross\-functional teams.
- Excellent communication and presentation skills with the ability to explain complex technical concepts to business stakeholders.
- Experience mentoring junior engineers or leading technical initiatives preferred.
This position is eligible to participate in the Company’s annual discretionary bonus plan, subject to the terms and conditions of the incentive program, based on individual and company performance metrics. This position may also be eligible for additional compensation in the form of equity grants pursuant to the Company’s long\-term incentive plan.
This posting is for an existing vacancy.
Benefits at all of our global offices are focused on physical, mental and financial wellness. We offer unique and progressive benefits, including a comprehensive global paid parental leave program that supports employees as they expand their families, free telemedicine and mental wellness support.
Restaurant Brands International and all of its affiliated companies (collectively, RBI) are equal opportunity and affirmative action employers that do not discriminate on the basis of race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or veteran status, or any other characteristic protected by local, state, provincial or federal laws, rules, or regulations. RBI's policy applies to all terms and conditions of employment. Accommodation is available for applicants with disabilities upon request.
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Restaurant Brands International, 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 $181,170 based on 12,692 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 $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Restaurant Brands International AI Hiring
Restaurant Brands International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Miami, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.