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About This Role
Are you curious about solving complex business challenges for a leading convenience retailer? Do you have a passion for cross functional collaboration? Then you may be the perfect addition to our team!
Cumberland Farms is a member of the EG America family of brands. EG America is one of the fastest-growing convenience store retailers in the United States, committed to becoming America’s #1 ‘one-stop’ destination. The business has an established pedigree of delivering excellent fuel, grocery and merchandise, and food service. Headquartered in Westborough, Massachusetts, our Company has grown to over 1,500+ locations across the United States employing over 18,000 team members. You can find us operating under the following store banners: Certified Oil, Cumberland Farms, Fastrac, Kwik Shop, Loaf N Jug, Minit Mart, Sprint Food Stores, Tom Thumb, Turkey Hill, and Quik Stop. Our headquarters in Westborough, MA is home to our Store Support Center, Company Warehouse, and Culinary Center.
What We Offer:
- Competitive Wages
- Work today, get paid tomorrow through our earned wage access program\*
- Paid Time Off
- Medical/Health/Dental Coverage
- 401K with Company Match
- Team Member Discounts
- Tuition Reimbursement
- Employee Assistance Program
- Health Savings Account
- Company Spirit Days
- Employee recognition and awards
- And much more!
Position Summary:
The District Manager In Training (DMIT) position is geared towards providing eligible candidates the training to be successful in the role of District Manager. As a District Manager you will be entrusted to guide a team of Store Managers in delivering an exceptional guest shopping experience, operational excellence, and a working environment that promotes engagement and living the Company values, making EG America the clear choice for our guest’s every day needs. In the District Manager role you will direct and oversee all area store personnel to achieve Region, Division and Company net profit performance objectives.
Position Training:
Phase I of training requires that the DMIT spend time completing foundational training required of every EG America team member: New Hire And Orientation Training. The DMIT will then be required to successfully complete the MIT Training program, gaining insight into the role of Store Manager or Restaurant Manager– the team they will be managing. The last stage of Phase I of the DMIT training program is to spend 2-3 months assigned to one location, managing the team and day to day store/restaurant operation. *(Internal Promotions from SM/RGM level are not required to complete Phase I).*
Phase II of training requires that the DMIT spend 6-8 weeks riding with the Designated DM Trainer, observing their job and working alongside them in the completion of their day to day duties, learning and practicing the duties and responsibilities of the District Manager. DMITs who have completed all training will become District Managers In Waiting and be assigned back in their home store in the capacity of leader of the unit until an area opens for them to be assigned to.
Responsibilities:
- Responsible for building a strategic plan for area to include appropriate staffing levels, development and performance management of all team personnel.
- Oversee team engagement and productivity over wide network of locations, fostering a working environment that supports team member retention and growth.
- Demonstrate leadership attributes to include: building and maintaining trust with the store teams by setting clear and measurable goals, holding self and others accountable, and communicating frequently and effectively.
- Build and develop a strong leadership team by: hiring or promoting store management candidates to prepare for future staffing needs, and ensuring that your current teams are receiving appropriate training, coaching, and feedback, leading by example.
- Analysis of financial reports, P&L, Gap analysis, etc. Monitoring current sales, expenses, store labor costs and inventory control. Evaluating and disseminating data for strategic gain, coaching Store Managers towards improving profitability.
- Weekly store visits to ensure compliance with Region, Division and Company standards regarding store conditions, store promotions, operational procedures and financial controls;
- Ensures area wide guest satisfaction and product quality while managing safety and security within the territory.
- Heavy emphasis on food service, increasing sales, monitoring food service standards and safety.
- Perform other duties as assigned at the discretion of the Region Manager.
- Must be able to perform the essential functions of this position with or without reasonable accommodations.
Working Relationships: Store team members, Region Manager, VP of Retail Operations, Human Resource Business Partner and Human Resource Centers of Excellence, Facilities Maintenance, Marketing, Risk Management, Environmental, Legal departments, etc. and vendors.
Minimum Education: High School or GED
Preferred Education: College degree in business, or a closely related field. May substitute for a portion of the required experience.
Minimum Experience: 10 years retail experience restaurant general management experience. Successful completion of the DMIT Program
Preferred Experience: 1-3 years multi-unit experience in c-store or restaurant environment
Licenses/Certifications:
Soft Skills:
- Excellent team building and leadership practices
- Strong communication and interpersonal skills
- Organizational skills and proficiency in Microsoft Word, Microsoft Excel, and ability to learn additional programs as needed
- Ability to multitask, prioritize and constructively handle various issues that arise
- Strong analytical skills
Travel:95% traveling from location to location
Hours & Conditions:
Typically Monday – Friday for a 48 hour work week (mirroring SM work week), however occasional weekend work may be required depending on the business needs.
Physical Requirements:
Ability to maneuver and regularly lift and or move up to 10 pounds, frequently lift and/or move up to 25 pounds, and occasionally lift and/or move up to 40 pounds. Ability to stand/walk 8 hours a day; reach overhead, bend, squat, twist, reach, grasp and grip..
Other:
- Must have a clean driving record
- Please indicate if willing to relocate
At EG America, it’s important that our employees reflect the world we live in and the communities we serve. We celebrate our differences, so your unique background and skillset could bring a wonderful new perspective to our team. If you have a passion for delivering exceptional results, thrive in a fast-paced corporate environment, and bring experience in business management or related areas, we'd love to meet you - even if you don't meet every single requirement.
*In the spirit of pay transparency, we’re sharing the base salary range for this position. Final pay within this range will be based on your skills, experience, and qualifications.*
*Base pay represents just one part of our total rewards approach. We’re proud to offer a variety of financial and non-financial benefits that invest in your overall growth, well-being, and career journey.*
Salary Context
This $65K-$72K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $170K across 217 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 Cumberland Farms, 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. This role's midpoint ($68K) sits 56% below the category median. Disclosed range: $65K to $72K.
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.
Cumberland Farms AI Hiring
Cumberland Farms has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Springfield, MA, US, Montpelier, VT, US. Compensation range: $72K - $85K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 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 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 $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 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 $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: 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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