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About This Role
Great Lakes BU \- Region 01 \- Market 03: 8578 Henry Clay Blvd, Clay, New York 13041Shift Availability
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Flexible Availability Time Type
Minimum Qualifications
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The minimum qualifications for a Store Manager are:
- High School diploma or GED preferred.
- Experience in retail sales preferred.
- Experience to perform the essential duties, responsibilities and working in the conditions described below.
- Ability to supervise and train for the use of equipment, tools and materials listed in the Customer Service Representative (CSR) and Assistant Site Manager (ASM) job description.
- Ability to supervise and manage the functions listed in the CSR and ASM job description.
- Ability to use computer, or acquire those skills necessary to use a computer at the site, which includes analysis of reports, inventory control, cash control, counseling notices, etc.
- A valid driver’s license and adequate transportation to/from bank and corporate management meetings.
- Ability to communicate (orally and in writing) in English.
- Perform other duties as assigned or delegated by his/her supervisor.
ESSENTIAL DUTIES, RESPONSIBILITIES AND SKILLS
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Leadership and Management
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- Recruit, hire and train positive, enthusiastic employees, ensuring excellent customer service.
- Develop, manage and assign tasks appropriately to ensure the site is clean, adequately stocked, organize and well kept based on Company standards.
- Maintain a professional and supportive image among subordinates and supervisor.
- Schedule employees within Company guidelines to maximize customer service and maintain site image.
- Implement non\-discriminatory related management skills while hiring, training, counseling, motivating and separating employees.
Site Relationships
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- Develop positive and professional relationships with all suppliers.
- Promote excellent service and resolve customer complaints in a timely, professional manner.
- Promote and ensure a safe, positive public image within the neighboring community.
Training and Development
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- Prepare on\-going and timely performance appraisals in writing for all employees, providing proper performance based feedback.
- Train all employees ensuring that customer service, site image and marketing execution meet Company standards.
- Train all employees on safety procedures and promote safety awareness.
Communication
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- Develop ways and means to ensure that all employees receive proper communication in a timely manner.
- Establish periodic on\-going communication meetings with all site employees and the Market Manager.
Organizing and Planning
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- Evaluate and develop specific action plans to address the needs of the site in order to reach the desired objectives.
- Organize and maintain all site files and manuals.
- Manage and supervise store employees to ensure that all required and requested reports due are completed accurately and timely.
- Manage and supervise store employees to ensure that all merchandise is stocked, attractively displayed and priced correctly.
- Ensure that all required employment related posters and signs are in a place that is easily accessible to all employees.
Financial
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- Analyze daily sales and expense information and take appropriate action to maximize sales and net profits.
- Budget and forecast P\&L lines, as well as understand and manage merchandise margins.
- Safeguard and account for all money received and disbursed.
- Perform all other financial analysis necessary to maximize sales and net profits.
Working Conditions
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- Performs approximately all work indoors but will be required to work outside in order to clean parking lots, gas pumps, take out garbage, etc.
- Be exposed to occasional cold temperature extremes while supervising or managing store employees performing occasional work in a walk\-in cooler and/or freezer.
- Be exposed to occasional noise.
- Work with a minimum of direction and supervision.
- At all times work as an effective manager, supervisor and leader.
THE ABOVE STATEMENTS REFLECT THE GENERAL QUALIFICATIONS/DUTIES AND/OR RESPONSIBILITIES NECESSARY TO IDENTIFY THE JOB AND ARE NOT NECESSARILY INTENDED TO SET FORTH ALL OF THE SPECIFIC REQUIREMENTS OF THE JOB.
NOTE: This job description may change periodically as required by business necessity, with or without advance notice to or consent by the employee.
Hiring Range: $20\.00 to $20\.76Circle K is an Equal Opportunity Employer.
The Company complies with the Americans with Disabilities Act (the ADA) and all state and local disability laws. Applicants with disabilities may be entitled to a reasonable accommodation under the terms of the ADA and certain state or local laws as long as it does not impose an undue hardship on the Company. Please inform the Company’s Human Resources Representative if you need assistance completing any forms or to otherwise participate in the application process.
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Salary Context
This $41K-$41K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →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 Circle K, 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. This role's midpoint ($41K) sits 75% below the category median. Disclosed range: $41K to $41K.
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.
Circle K AI Hiring
Circle K has 32 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Wheaton, IL, US, Chester, SC, US, Fox Lake, IL, US. Compensation range: $39K - $41K.
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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