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About This Role
Company Description ABOUT THE JOB
Do you know why Domino's Pizza hires so many drivers? Well, aside from the fact that our delicious pizza is the perfect solution for dinner for families all across the nation \- we do it because we can. What do we mean by that, you ask? We mean that we offer a great flexible schedule that offers the hours you're looking for. That means you're free when you need to be. You'll have plenty of time left over for school, to hang with your friends, or whatever. Even if you need a second job for some extra cash, Domino's Pizza is the perfect place for you.
Right now Domino's is looking for qualified drivers to staff stores in your area. We're growing so fast it's hard to keep up, and that means Domino's has lots of ways for you to grow (if that's what you want), perhaps to management, perhaps beyond. Whether it's your hobby, main\-gig, or supplemental job, apply online. We're bound to have just the thing for you.
ADVANCEMENT
Many of our team members began their careers as delivery drivers and today are successful Domino's franchise owners. From delivery driver to management, general manager to franchisee or Manager Corporate Operations, our stores offer a world of opportunity.
DIVERSITY
Our mission is to recognize, appreciate, value and utilize the unique talents and contributions of all individuals. To create an environment where all team members, because of their differences, can reach their highest potential.
SUMMARY STATEMENT
We take pride in our team members and our team members take pride in Domino's Pizza! Being the best pizza delivery company in the world requires exceptional team members working together. At Domino's Pizza, our people come first!
Job Description General Job Duties For All Store Team Members
- Operate all equipment.
- Stock ingredients from delivery area to storage, work area, walk\-in cooler.
- Prepare product.
- Receive and process telephone orders.
- Take inventory and complete associated paperwork.
- Clean equipment and facility approximately daily.
Training
Orientation and training provided on the job.
Communication Skills
- Ability to comprehend and give correct written instructions.
- Ability to communicate verbally with customers and co\-workers to process orders both over the phone and in person.
Essential Functions/Skills
- Ability to add, subtract, multiply, and divide accurately and quickly (may use calculator).
- Must be able to make correct monetary change.
- Verbal, writing, and telephone skills to take and process orders. Motor coordination between eyes and hands/fingers to rapidly and accurately make precise movements with speed.
- Ability to enter orders using a computer keyboard or touch screen.
- Navigational skills to read a map, locate addresses within designated delivery area.
- Must navigate adverse terrain including multi\-story buildings, private homes, and other delivery sites while carrying product.
Work Conditions
EXPOSURE TO
- Varying and sometimes adverse weather conditions when removing trash and performing other outside tasks.
- In\-store temperatures range from 36 degrees in cooler to 90 degrees and above in some work areas.
- Sudden changes in temperature in work area and while outside.
- Fumes from food odors.
- Exposure to cornmeal dust.
- Cramped quarters including walk\-in cooler.
- Hot surfaces/tools from oven up to 500 degrees or higher.
- Sharp edges and moving mechanical parts.
- Varying and sometimes adverse weather conditions when delivering product, driving and couponing.
SENSING
- Talking and hearing on telephone. Near and mid\-range vision for most in\-store tasks.
- Depth perception.
- Ability to differentiate between hot and cold surfaces.
- Far vision and night vision for driving.
TEMPERAMENTS
The ability to direct activities, perform repetitive tasks, work alone and with others, work under stress, meet strict quality control standards, deal with people, analyze and compile data, make judgements and decisions.
Qualifications JOB REQUIREMENTS AND DUTIES
You must be at least 18 years of age and have a valid driver's license with a safe driving record meeting company standards as well as access to an insured vehicle which can be used for delivery. You should possess navigational skills to read a map, locate addresses within designated delivery area and must be able to navigate adverse terrain including multi\-story buildings.
Additional Information
PHYSICAL REQUIREMENTS, including, but not limited to the following:Standing
Most tasks are performed from a standing position. Walking surfaces include ceramic tile "bricks" with linoleum in some food process areas. Height of work surfaces is between 36" and 48".
Walking
For short distances for short durations
Delivery personnel must travel between the store and delivery vehicle and from the delivery vehicle to the customer's location.
Sitting
Paperwork is normally completed in an office at a desk or table
Lifting
Bulk product deliveries are made twice a week or more and are unloaded by the team member using a hand truck.
Deliveries may include cases of ingredients and supplies weighing up to 50 pounds with dimensions of up to 3' x 1\.5'.
Cases are usually lifted from floor and stacked onto shelves up to 72" high.
Carrying
Large cans, weighing 3 pounds, 7 ounces, are carried from the workstation to storage shelves.
Occasionally, pizza sauce weighing 30 pounds is carried from the storage room to the front of the store.
Trays of pizza dough are carried three at a time over short distances, and weigh approximately 12 pounds per tray.
During delivery, carry pizzas and beverages while performing "walking" and "climbing" duties.
Pushing
To move trays which are placed on dollies.
A stack of trays on a dolly is approximately 24" \- 30" and requires a force of up to 7\.5 pounds to push.
Trays may also be pulled.
Climbing
Team members must infrequently navigate stairs or climb a ladder to change prices on signs, wash walls, perform maintenance.
During delivery of product, navigation of five or more flights of stairs may be required.
Stooping/Bending
Forward bending at the waist is necessary at the pizza assembly station.
Toe room is present, but workers are unable to flex their knees while standing at this station.
Duration of this position is approximately 30 \- 45 seconds at one time, repeated continuously during the day.
Forward bending is also present at the front counter and when stocking ingredients.
Crouching/Squatting
Performed occasionally to stock shelves and to clean low areas.
Reaching
Reaching is performed continuously; up, down and forward.
Workers reach above 72" occasionally to turn on/off oven controls, change prices on sign, and lift and lower objects to and from shelves.
Workers reaching down to perform such tasks as scooping cornmeal from a plastic barrel, or washing dishes.
Workers reach forward when obtaining topping ingredients, cleaning work surfaces, or answering phones.
Driving
Deliver pizzas within a designated delivery area. A Team Member may make several deliveries per shift.
Machines, Tools, Equipment, Work Aids
Team Members may be required to utilize pencils/pens, computers, telephones, calculators, TDD equipment, pizza cutter and pizza peel.
Driving Specific Job Duties
Deliver product by car and then to door of customer.
Deliver flyers and door hangers.
Requires
Valid driver's license with safe driving record meeting company standards.
Access to insured vehicle which can be used for delivery.
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 10,872 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Domino'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 $154,000 based on 8,743 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Domino's AI Hiring
Domino's has 21 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Somerville, MA, US, Honolulu, HI, US, Middlebury, VT, US. Compensation range: $31K - $52K.
Location Context
Across all AI roles, 11% (1,212 positions) offer remote work, while 9,626 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 10,872 open positions tracked in our dataset. By seniority: 871 entry-level, 6,773 mid-level, 2,013 senior, and 1,215 leadership roles (Director, VP, C-Level). Remote roles make up 11% of the market (1,212 positions). The remaining 9,626 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 10,872 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (9,872), AI Software Engineer (262), Data Scientist (256). 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 (871) are outnumbered by mid-level (6,773) and senior (2,013) 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 1,215 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 11% of all AI roles (1,212 positions), with 9,626 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $293,500 median, while Prompt Engineer roles sit at $145,600. 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 (1,782 postings), Aws (1,065 postings), Azure (796 postings), Rag (710 postings), Gcp (617 postings), Pytorch (602 postings), Prompt Engineering (516 postings), Tensorflow (511 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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