Interested in this AI/ML Engineer role at 7-Eleven?
Apply Now →About This Role
*7\-Eleven is an iconic family of brands with over 86,000 locations, surpassing every retailer in the world. We revolutionize convenience, restaurants and fuel through cutting edge innovation — working hard to be the customer's first choice. 7\-Eleven empowers our employees to "activate awesome" and make a meaningful impact in their stores and communities every day. If you're ready to grow, lead and make a difference, come join our team and help shape the future of convenience.*
Overview
7\-Eleven is looking for a Senior Manager, AI, to join the Enterprise AI Team!
At 7\-Eleven, we’re creating the next generation of convenience, and we know that the best way to do that is with great people. If you get excited about working on challenging problems and owning a solution from concept to implementation, read on.
About the job:
You’ll be joining a multidisciplinary team of scientists, researchers and engineers who research and innovate on latest technology to create awesome data and AI products that millions of people will experience every day. Managers on the Enterprise AI team at 7\-ELEVEN have a large amount of experience across multiple areas and lead the technical direction of the team, guiding developers and communicating team progress alongside the Product Manager and Design Lead. They can plan, coordinate, and deliver large tasks spanning multiple systems, and are strong communicators. They actively coach and mentor developers on their development team and can identify and resolve issues with technology and product processes.
As a Senior Manager, you will not only help teams navigate technical problems but grow and coach your reports to achieve a greater degree of impact throughout the organization. Note that most Sr managers will typically only spend 0 \-10% of their time programming, with the bulk of their time being committed to architecting/planning, mentoring, team morale, budget management, and communication with internal and external stakeholders.
Key Duties and Responsibilities
- Lead the research, prototyping, and development of AI solutions for retail business challenges.
- Manage the full lifecycle of machine learning projects from conception to production deployment.
- Establish best practices for model development, validation, deployment, and monitoring.
- Mentor and coach team members to enhance their technical skills and career growth.
- Partner with business stakeholders to align machine learning initiatives with strategic objectives.
- Oversee the development of ML pipelines and infrastructure to support scalable model deployment.
- Ensure high standards for data quality, model performance, and ethical AI practices.
- Evaluate and recommend appropriate machine learning technologies, frameworks, and approaches.
- Drive innovation by staying current with emerging AI/ML technologies and research.
- Communicate complex technical concepts to non\-technical stakeholders effectively.
- Manage project timelines, resources, and deliverables to ensure successful implementation
Basic Qualifications:
- Bachelor's degree in computer science, Machine Learning, Statistics, Mathematics, or a related field; Master's or PhD preferred.
- 8\+ years of experience working in machine learning or data science roles with at least 2\+ years of experience in a Technical Managerial role.
- 2\+ years of experience managing a team of machine learning engineers or data scientists in a management or technical capacity, with demonstrated delivery.
- Strong understanding of the foundations of Data Science and Machine Learning, with experience in building diverse types of Machine Learning applications across Natural Language Processing, Structured Tabular Data and/or Computer Vision.
- Experience with the full machine learning lifecycle, from data collection to model deployment, with demonstrated experience delivering ML projects with measurable business impact.
- Strong knowledge of deploying AI/ML applications in production, including feature engineering, model selection, model deployment, and machine learning operations (ml\-ops).
- Strong understanding of LLM models, LLM Engineering architecture and ecosystem, including context, tools, skills, harness etc., with demonstrated delivery.
Preferred Qualifications:
- Experience managing, coaching and mentoring people, with demonstrable ability to build team culture.
- Experience working in retail, e\-commerce, or consumer\-facing technology preferred.
- Experience with Databricks.
- Experience building and scaling custom Chatbots and Multi\-Agent systems successfully.
Position not eligible for sponsorship.
Position is onsite and based in Irving TX.
\#LI\-PG1
*This job description is intended to describe the general nature and level of the work being performed by the individuals assigned to this job. This is not an exhaustive list of all duties and responsibilities. Management reserves the right to amend and change the duties and responsibilities of this job to meet business and organizational needs, as necessary.*
*If an hourly or salary range is included in this ad it represents the range 7\-Eleven in good faith believes is the range of compensation for this role at the time of this posting. The Company may ultimately pay more or less than the posted range. This range is only applicable for jobs to be performed in this state. This range may be modified in the future. No amount is considered to be wages or compensation until such amount is earned, vested, and determinable under the terms and conditions of the applicable policies and plans. The amount and availability of any bonus, commission, long\-term incentive compensation, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole discretion unless and until paid and may be modified at the Company’s sole discretion, consistent with the law.*
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At 7-Eleven, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
7-Eleven AI Hiring
7-Eleven has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Irving, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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