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About This Role
AI Engineer (On\-site) \- Plano, TX
Welcome to Ziosk, where we empower restaurants to focus on what matters most: the guest experience!
Have you ever used a tablet to pay at a restaurant? We pioneered the pay\-at\-the\-table concept and we're cooking up a plan to transform the restaurant industry. Our recipe for success has been adapting and growing to exceed the needs of our clients, such as Olive Garden, Texas Roadhouse, Chili's and more – helping them create an experience that keeps guests coming back. Today we have a full menu of solutions, from hardware to software to cloud\-based and AI driven products, all focused on helping them create the best guest experience possible to grow their bottom line.
Our secret sauce? Our people! Every day, they're cooking up bold solutions, making Ziosk the leading pay\-at\-the\-table provider in the industry.
Want a seat at our table? Ziosk is looking for a highly experienced AI Engineer to join our Enterprise Datadepartment. Ziosk's AI bench is currently a team of one — and we're growing it. You will work alongside our Senior AI/ML Engineer on production AI/ML systems, and contribute to flagship AI products like the IPL Ad Optimizer and Contextual Intelligence engine. This is hands\-on engineering: we ship to production and iterate from real telemetry.
The Main Course – Responsibilities
- Build, train, evaluate, and deploy production AI/ML models alongside the Senior AI/ML Engineer — including the IPL Ad Optimizer and successor models.
- Own end\-to\-end development of agent products: requirements, architecture, prompt engineering, evals, deployment, and monitoring.
- Lead the Documentation Agent project and contribute to the QA Agent in partnership with QA Automation.
- Share the embedded POD on\-call rotation for AI products; respond to model performance regressions, evaluate drift, and ship fixes.
- Define and run model evaluations — both offline (golden sets, regression suites) and online (shadow deployments, A/B tests, telemetry\-driven iteration).
- Partner with Full\-Stack Data BI Engineers to embed ML\-powered features inside Dash applications, including anomaly detection, predictive metrics, and AI\-generated narratives.
- Contribute to MLOps platform standards — model registry hygiene, CI/CD for models, monitoring, and drift detection.
What You Bring To The Table – Qualifications
Required
- 5\+ years of professional software engineering experience, with at least 3 years building production AI/ML or LLM\-powered systems.
- Strong Python proficiency and production experience with modern ML frameworks (PyTorch, TensorFlow, scikit\-learn) and ML platforms (MLflow, Databricks ML, SageMaker, or equivalent).
- Hands\-on experience building production LLM agents — prompt engineering, tool calling, and agent orchestration (LangGraph, AutoGen, Anthropic API, OpenAI API, or similar).
- Experience designing and running model evaluations, including classical ML metrics and LLM evals (golden sets, automated graders, A/B testing).
- Working knowledge of MLOps fundamentals: model versioning, CI/CD for models, drift detection, monitoring, and rollback patterns.
- Strong software engineering fundamentals: version control, testing, code review, and on\-call experience in production AI systems.
Preferred
- Direct production experience with Anthropic Claude API, OpenAI API, or other frontier LLM providers.
- Experience building agentic systems — multi\-step reasoning, tool use, memory, and error recovery.
- Familiarity with Databricks ML, Unity Catalog ML lineage, and lakehouse\-native ML patterns.
- Background in restaurant, retail, hospitality, or other guest\-facing applications of ML such as recommendation, personalization, or demand forecasting.
- Active use of AI\-assisted development tooling (Cursor, Claude Code, GitHub Copilot) in your own engineering work.
- Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or equivalent practical experience.
*Ziosk* *is an Equal Opportunity employer offering competitive benefits and compensation. Candidates must be eligible to work in the U.S. and be able to commute daily to Plano, TX.* *No agencies or third\-party recruiters, please**.*
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 Ziosk, 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. Mid-level AI roles across all categories have a median of $165,000.
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.
Ziosk AI Hiring
Ziosk has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, 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
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