Interested in this AI/ML Engineer role at Fogo de Chao?
Apply Now →About This Role
At Fogo de Chão, we strive to give our guests an unforgettable dining experience of discovery while showcasing the Culinary Art of Churrasco. Our mission is to ignite fire and joy to care for our team, our guests, and our communities.
We believe better futures start when we bring our best to the table every day to Feed a Purposeful Future – starting with our team members. We feed our teams with fulfilling job opportunities, making space around the table so everyone feels welcome. At Fogo, we’ll provide you with a fulfilling career with professional and personal growth opportunities at every level. Our values of teamwork, integrity, excellence, humility, and Deixa Comigo (we’ve got you!) are more than just words, it’s how we do things every day.
We are hiring a Director, People Experience, Technology, \& AI Transformation to simplify how HR work gets done, leverage automation and AI responsibly, and build people technology that enables leaders to focus on developing teams and delivering exceptional Guest Experiences.
This individual contributor role operates as a department of one with broad enterprise influence across HR, Operations, IT, Enterprise Data \& Analytics, Finance, Legal/Compliance, vendors, and field leadership.
The ideal candidate brings deep HR domain expertise, technology fluency, AI curiosity, change enablement capability, and the discipline to translate people needs into scalable, data\-informed products, workflows, and adoption plans.
What You Will Own
### Simplify Work to Elevate Hospitality
- Redesign HR processes to enable connection, recognition, and high standards.
- Eliminate redundant approvals, manual data entry, and low\-impact reporting.
- Ensure solutions increase time spent with Guests and Teams.
- Scope current\-state HR processes and workflows to identify friction points, manual workarounds, duplication, and areas prone to human error; use findings to design simplified, future\-state workflows that are scalable, intuitive, and adoption\-ready.
### Eliminate Low\-Value Work at Scale
- Conduct time studies across Above Restaurant Operations Leadership, Restaurant Managers, and HRBPs
- Identify top operational inefficiencies
- Drive elimination, automation, or self\-service adoption
### Deploy Practical AI \& Workforce Technology
- Implement high\-impact use cases:
+ Hourly “human in the loop” hiring automation
+ Automated onboarding workflows
+ Team Member self\-service
+ Manager support/coaching tools
- Partner with IT while owning prioritization and adoption.
- Partner with HR Centers of Excellence, Operations, Enterprise Data \& Analytics, and IT to translate restaurant and people pain points into AI\-enabled use cases, product requirements, workflows, data requirements, integration needs, governance inputs, and adoption plans.
- Evaluate emerging people technology, automation, and AI capabilities through a practical lens for risk, usability, data quality, compliance, security, scalability, and Team Member experience.
### Own the Enterprise People Technology Project Map
- Own and maintain the full people technology transformation project map, including active initiatives, future\-state opportunities, owners, milestones, dependencies, risks, decisions needed, sequencing, resourcing assumptions, and expected value.
- Establish clear governance, intake, prioritization, status reporting, and escalation routines so leaders can make informed tradeoffs, and the work remains aligned, visible, and actionable.
- Translate a complex roadmap into simple executive updates, field\-ready communications, vendor priorities, and cross\-functional action plans.
### Productize HR Programs
- Consult with HR leaders and cross\-functional partners to simplify performance, engagement, and development programs so they are scalable, practical, and easy for restaurant teams to adopt.
- Minimize and/or eliminate corporate\-heavy processes that do not translate to restaurants.
### Drive Adoption Through Trust and Simplicity
- Partner with Above Restaurant Operations Leadership and General Managers.
- Pilot, refine, and scale solutions.
- Address resistance to change and inconsistency by leaning into the ADKAR model
- Build change enablement plans that include stakeholder alignment, communications, training, behavior reinforcement, adoption measurement, and field feedback loops.
- Serve as a credible translator across HR, Operations, IT, Enterprise Data \& Analytics, vendors, and Restaurant Leaders so solutions are technically sound, data\-informed, operationally simple, secure, scalable, and human\-centered.
What You Bring
### Experience
- 8–12\+ years in HR transformation, workforce tech, or operations.
- Multi\-unit, high\-volume environment experience preferred.
- Proven ability to implement change at scale.
- Demonstrated HR domain expertise across multiple functions, including talent acquisition, onboarding, learning, performance, engagement, total rewards, HR operations, workforce analytics, and compliance\-sensitive people processes.
### Capabilities
- Process simplification expertise.
- Business process optimization expertise, including design thinking, current\-state assessment, future\-state workflow design, root\-cause analysis, error\-proofing, simplification, and adoption\-ready process documentation.
- Strong data orientation.
- Field\-first mindset.
- Hands\-on people technology fluency across HRIS, ATS, LMS/LXP, workforce management, case management, analytics, automation, and AI\-enabled productivity tools; able to define requirements, assess vendors, partner with technical teams, and guide implementation decisions.
- Comfort working with Enterprise Data \& Analytics and IT on data governance, system integrations, reporting logic, analytics use cases, AI enablement, security, architecture, and roadmap prioritization.
- Enterprise roadmap and portfolio ownership; able to manage workstreams, dependencies, milestones, risks, vendors, decisions, and tradeoffs without relying on a large team or formal project management office.
- High\-influence individual contributor with a people\-experience design mindset; able to set direction, create structure, drive alignment, and hold workstreams accountable through credibility, clarity, and disciplined follow\-through.
### Mindset
- Challenges legacy work constructively.
- Moves quickly and iterates.
- Makes decisions with imperfect data.
- Curious and responsible AI mindset; understands where AI can create value, where human judgment must remain central, and how to build confidence through transparency, governance, and practical outcomes.
Operating Model
-------------------
- Reports to the Chief People Officer.
- Dotted\-line partnership with the VP, Enterprise Data \& Analytics and the VP, IT.
- Individual contributor role; operates as a department of one with broad influence across HR, Operations, IT, Enterprise Data \& Analytics, Finance, Legal/Compliance, vendors, and field leadership.
- Self\-funding transformation through efficiency gains.
- Serves as the HR business owner and transformation integrator for people technology priorities, leading the roadmap, operating rhythm, executive updates, vendor coordination, dependency management, adoption planning, and benefits tracking in partnership with technical and functional leaders.
- This role partners across the Human Capital portfolio to inform investment recommendations, roadmap priorities, vendor requirements, business cases, and value realization for all people\-related technology products under the Chief People Officer’s scope.
Deixa Comigo: Role Clarity \& Collaboration
This role connects business needs, people experience, technology design, data enablement, and adoption into one clear, actionable roadmap while preserving clear accountability across partner functions.
- This role leads: the people technology roadmap, HR business requirements, prioritization recommendations, user experience design, AI\-enabled HR use case development, project\-map visibility, adoption planning, and benefits tracking in partnership with accountable functional leaders, including the VP, HR Operations.
- With Enterprise Data \& Analytics: co\-creates people data requirements, reporting standards, analytics use cases, dashboard needs, data quality priorities, governance inputs, and responsible AI enablement.
- With IT: co\-creates architecture alignment, integration requirements, security and access considerations, implementation feasibility, vendor technical due diligence, and deployment planning.
- With HR Operations: ensures redesigned processes are operationally sound, scalable, compliant, and service\-delivery ready; HR Operations retains day\-to\-day process execution accountability and owns the HR technology budget, with this role informing prioritization, business cases, vendor needs, and benefits tracking.
- With Organizational Enablement \& PMO: aligns change strategy, project governance, communications, training, sequencing, stakeholder engagement, and enterprise project standards; PMO retains enterprise methodology and broader portfolio governance.
- With Field HR: validates restaurant pain points, pilots solutions, supports field adoption, and ensures tools and processes work in restaurants; Field HR retains field\-facing HR leadership and issue escalation.
Cultural Expectations
-------------------------
- Balance hospitality and performance
- Preserve personal connection while improving consistency
- Modernize without losing brand identity
First 90 Days
-----------------
- Conduct an HR friction audit across restaurants.
- Identify the top three time drains.
- Complete a current\-state business process assessment, including workflow mapping, friction\-point identification, human\-error risk areas, manual workaround inventory, and prioritized optimization opportunities.
- Map the current HR technology ecosystem, data flows, vendor dependencies, user pain points, and priority AI or automation opportunities.
- Build a consolidated people technology transformation project map with active and proposed initiatives, owners, dependencies, risks, decisions needed, timing, value case, and adoption requirements.
- Launch one to two pilot solutions and build credibility through quick wins.
Success Scorecard (First 12 Months)
---------------------------------------
### Efficiency \& Capacity
- Reduce manager administrative time by 20–30%
- Reduce HR transactional workload by 65%\+
### Talent Outcomes
- Reduce time\-to\-fill by 20%
- Achieve 90%\+ onboarding completion within 14 days
- Improve 90\-day retention
### Adoption \& Experience
- Increase self\-service adoption
- Improve Restaurant Manager satisfaction with HR processes
### Program Effectiveness
- 95%\+ performance review completion
- 85%\+ engagement survey participation
### Transformation Impact
- Eliminate 5–7 low\-value processes
- Deliver 18\-month transformation roadmap
### Roadmap, Governance \& Execution Discipline
- Deliver and maintain an enterprise\-visible people technology project map with clear priorities, owners, dependencies, risks, decision points, and sequencing.
- Create a practical governance cadence that enables faster decisions, tighter alignment, and consistent visibility across HR, Operations, IT, Enterprise Data \& Analytics, Finance, Legal/Compliance, vendors, and field leadership.
- Track adoption, efficiency gains, experience improvements, and benefits realization so transformation progress is measured by business value, not activity alone.
This role is designed for a builder who can rethink how work happens, eliminate inefficiencies, and deliver practical solutions that restaurant teams embrace.
*This position is fully on\-site at our Addison, TX office.*
*Medical, Dental, and Vision insurance are available for full\-time Team Members on the first of the month following their start date. Additionally, company\-paid Life Insurance and Short\-Term Disability are provided where allowed. We offer a comprehensive voluntary benefits package including Critical Illness, Hospital Indemnity, and Accident Coverage.*
*Fogo de Chão is an Equal Opportunity \& E\-Verify Employer*
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 Fogo de Chao, 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 in Demand for This Role
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. Director-level AI roles across all categories have a median of $274,554.
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.
Fogo de Chao AI Hiring
Fogo de Chao has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, 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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