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About This Role
Job Description:
We Deliver the Goods:* Competitive pay and benefits, including Day 1 Health \& Wellness Benefits, Employee Stock Purchase Plan, 401K Employer Matching, Education Assistance, Paid Time Off, and much more
- Growth opportunities performing essential work to support America’s food distribution system
- Safe and inclusive working environment, including culture of rewards, recognition, and respect
Position Purpose:
This is an entry level driver training role. This driver is responsible for learning to and/or gaining experience in driving a tractor trailer or straight truck intrastate and/or interstate on local, over\-the\-toad (OTR), shuttle, and/or overnight routes to deliver and unload various food and food related products to customers. All routes are expected to be completed safely in accordance with all Company policies and Department of Transportation (DOT) regulations. The Driver Trainee communicates and interacts with customers, vendors and co\-workers professionally ensuring all services and duties are executed in accordance with preferred work methods and customer service practices. Functions as a team member within the department and organization, as required, and performs any duty assigned to best serve the company.
Position Responsibilities:* Attends and successfully completes PFG Entry Level Driver Trainee or Dock to Driver Training Program as required. All training documentation is completed and maintained per requirements.
- Rides\-with and assists driver trainer in executing deliveries as required. Follow all instructions and directions provided by driver trainer.
- Perform all required safety checks (i.e. pre/post trip) including inspections of tractor/truck and trailer according to Department of Transportation (DOT) regulations; inspect tractor/truck and trailer to insure they meet company safety standards and take appropriate action as needed. Report all safety issues and/or repairs required.
- Follow all DOT regulations and company safe driving guidelines and policies. Immediately report any and all safety hazards.
- Inspect trailer for properly loaded and secured freight. Perform count check of items and check customer invoices of products that have been loaded. Check and complete in an accurate and in legible fashion all required paperwork associated with freight. Move tractor to the loading dock and attach preloaded trailer as needed.
- Drive to and deliver customer orders according to predetermined route delivery schedule.
- Unload products from the trailer, transport items into designated customer storage areas. Perform damage control checks on items, scanning and contact supervisor about removing orders according to company policy. Verify delivery of items with customer and obtain proper signatures. Collect money (cash or checks) where required. Load customer returns on to trailer and secure trailer doors.
- Ensure that tractor, trailer and freight are appropriately locked and/or secured at all times.
- Unload damaged goods and customer returns and bring to the driver check\-in and complete necessary paperwork. Unload all equipment, materials and remove trash from trailers as required.
- Complete daily record of hours of service and enter in log in accordance with Federal DOT, state and company requirements.
- Perform general housekeeping duties in tractor, loading dock area and keep trailers clear and clean as required. At the end of the shift secure all equipment and complete all necessary paperwork.
- Performs other related duties as assigned.
Qualifications:
High School Diploma/GED or Equivalent
- Internal PFG Candidates: 1 year of service in good standing as outlined in the PFG Entry Level Driver Trainee or Dock to Driver Training Program, able to attain CDL Permit and DOT Health Card
- External Driver School Direct Hire: 0\-6 months experience with a CDL Permit
- External Entry Level Hire: 6\-12 months with a CDL Permit
- Valid CDL Permit or CDL A or B
- Must be 21 years of age
- Meet all State licensing and/or certification requirements
(where applicable)
- Clean Motor Vehicle Report (MVR) for past 3 years
- Pass post offer drug test
- Pass road test
- Attains or has valid current DOT Health Card
- Able to hand\-lift and utilize two\-wheeler, lift gate and/or other equipment to move and/or stack product cases/freight of varying size and weight throughout shift; product generally ranges from approximately 60 to 90 pounds, depending on the location
Company description
Performance Foodservice, PFG’s broadline distributor, maintains a unique relationship with a variety of local customers, including independent restaurants and hotels, healthcare facilities, schools, and quick\-service eateries. A team of sales reps, chefs, consultants, and other experts builds close relationships with customers — providing advice on improving operations, menu development, product selection, and operational strategies. The Performance team delivers delicious food but also goes above and beyond to help independent restaurant owners achieve their dreams.Awards and Accolades
Performance Food Group and/or its subsidiaries (individually or collectively, the "Company") provides equal employment opportunity (EEO) to all applicants and employees, regardless of race, color, national origin, sex, marital status, pregnancy, sexual orientation, gender identity, religion, age, disability, genetic information, veteran status, and any other characteristic protected by applicable local, state and federal laws and regulations. Please click on the following links to review: (1\) our EEO Policy; (2\) the "EEO is the Law" poster and supplement; and (3\) the Pay Transparency Policy Statement.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Performance Foodservice, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Performance Foodservice AI Hiring
Performance Foodservice has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Cincinnati, OH, US, Morristown, TN, US, Destin, FL, US. Compensation range: $64K - $150K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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