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About This Role
Groupon is a marketplace where customers discover new experiences and services everyday and local businesses thrive. To date we have worked with over a million merchant partners worldwide, connecting over 16 million customers with deals across various categories. In a world often dominated by e\-commerce giants, we stand out as one of the few platforms uniquely committed to helping local businesses succeed on a performance basis.
Groupon is on a radical journey to transform our business with relentless pursuit of results. Even with thousands of employees spread across multiple continents, we still maintain a culture that inspires innovation, rewards risk\-taking and celebrates success. The impact here can be immediate due to our scale and the speed of our transformation. We're a "best of both worlds" kind of company. We're big enough to have the resources and scale, but small enough that a single person has a surprising amount of autonomy and can make a meaningful impact.
#### About the Role:
Most roles at the start of a career come with an onboarding deck and a defined task list. This one does not. Groupon is rebuilding how it sells — from the ground up, with AI — and the person in this role is part of that build from day one.
The vehicle is Project Foundry: a production fleet of AI agents designed to give Groupon a parallel sales force that operates 24/7 and to make every human rep sharper the moment they step into a deal. Two layers: the first runs full sales motions autonomously — outbound, inbound triage, reactivation — so reps receive warm opportunities rather than cold lists. The second equips reps at the handoff point — context surfaced, deal history ready, next action suggested.
Reporting directly to the CSO, the AI Sales Associate learns the full architecture, contributes to both layers, and progressively takes end\-to\-end ownership of agents in production. You are not joining a team that advises the business on AI. You are building the system that is the business. There is no playbook. You help write it.
North Star
Ship AI agents that run on real merchant data, prove their value in measurable terms, and make the sales organisation faster and sharper than it would be without them. The build compounds — every signal, every call, every conversion feeds back into what you build next. You are here to make that happen.
What You'll Do:
- Build across both layers of the fleet — Layer 1: agents that run autonomously without rep involvement — outbound sequencing, lead prioritisation, inbound triage, reactivation. Reps receive warm opportunities, not cold lists. Layer 2: agents that equip reps at the moment they step in — account context surfaced, deal history ready, next action suggested. You learn how both layers work and progressively contribute to each.
- Identify what to build and make the case for it — You are not waiting to be given a task list. You look at the sales organisation, find the highest\-value problem an agent could solve, and bring a structured proposal to the CSO. Judgment about what is worth building matters as much as the ability to build it.
- Test, measure, and iterate — You run experiments on real merchant data, measure whether what you built is working, identify failure modes, and refine. Every agent you touch has a documented performance trail. You do not ship and move on.
- Mine call transcripts for agent inputs — Process sales call recordings to extract patterns, category signals, and performance data. This is raw material for the agents you build — not a separate analytics workstream. You surface insights from it and design around them.
- Document and maintain what you ship — Every agent you deploy has a before/after record. You keep it current. You present it. You own what you built — not just while you're building it.
What You Bring:
- A degree from an Ivy League or equivalent top\-tier university — computer science, data science, economics, mathematics, or a field that taught you to think in systems. The subject matters less than the rigor of how you were trained to think.
- You have built an AI agent or automated workflow — not a class project. Something you designed, built, and iterated on because you wanted to see if it would work. You can explain what it did, what broke, and what you changed. The bar is not commercial success. The bar is that you shipped something real.
- Technical capability to build: you have used LLM APIs (OpenAI, Anthropic, or equivalent), built prompt architectures, and connected systems together. Python or equivalent. You do not need to be a software engineer — but you need to be able to build a working agent without asking someone else to write the code.
- Commercial curiosity. You read about how businesses make money. You ask why something converts and why something else doesn't. You do not need a sales background — you need the instinct to connect what you build to a GP outcome.
- Rigor with data. You do not accept a number without understanding where it came from. You build your own measurement frameworks when none exist.
Who You Are:
- Builder first — your instinct is to build, not to ask for permission. You have side projects because you were curious, not because someone assigned them.
- AI\-native, not AI\-curious — you have spent real time understanding what these systems can and cannot do. You know the difference between a demo and a deployed agent.
- Comfortable with ambiguity — you can start without a complete brief, identify the next right step, and keep moving. You learn faster by doing than by waiting for clarity.
- Commercially curious — you connect what you build to a business outcome. You ask why something converts and why something doesn't. Technology is the means; impact is the goal.
*You are early in your career. That is the point. If you don't tick every box but you build, learn fast, and want to work on something that doesn't yet exist — we want to hear from you.*
How We Operate:
Five principles. Non\-negotiable.
- Extreme Ownership — if an agent you're responsible for underperforms, you own the diagnosis and the fix — not the vendor, not the backlog.
- Speed Over Comfort — you ship an experiment before you're sure it will work. You learn from live data, not from waiting.
- Impact Obsessed — every hour of your time should connect to a GP outcome — directly or as a clear step toward one.
- Simplify to Scale — you document what you build so it can be used by someone else. Complexity you create and don't explain is debt.
- Disciplined — you keep your data clean, your experiments rigorous, and your performance logs current. Good intentions without rigour produce noise.
How We Measure Your Success:
- Agent shipped: first working agent in production by day 90, with a documented hypothesis, performance data, and an honest assessment of what worked and what didn't
- Quality of proposals: structured agent proposals to the CSO — ranked by commercial impact, grounded in data, specific about what gets built and how it gets measured
- Experiment rigour: every agent you deploy has a defined success metric set before go\-live — not retrofitted afterward
- Learning progression: demonstrated improvement in AI build quality, commercial reasoning, and stack fluency quarter over quarter — assessed directly by the CSO
- Contribution to the fleet: at least one documented insight per month surfaced from call transcripts or agent performance data — standalone, with a recommendation
#### The Details:
- Location: Downtown Chicago (hybrid, 3 days a week in\-office)
+ Alternate locations: Can be remote for the right fit
- Salary Range:$70,000 – $110,000 (depending on experience), plus eligibility to participate in a performance\-based bonus program.
- Benefits: Medical, dental, vision, EAP, 401(k) match, ESPP, life and disability insurance, FSAs, flexible PTO, and more.
Groupon is an AI\-First Company
We're committed to building smarter, faster, and more innovative ways of working—and AI plays a key role in how we get there. We encourage candidates to leverage AI tools during the hiring process where it adds value, and we're always keen to hear how technology improves the way you work. If you're passionate about AI or curious to explore how it can elevate your role—you'll be right at home here.
Groupon's purpose is to build strong communities through thriving small businesses. To learn more about the world's largest local e\-commerce marketplace, click here. You can also find out more about us in the latest Groupon news as well as learning about our DEI approach. If all of this sounds like something that's a great fit for you, then click apply and join us on a mission to become the ultimate destination for local experiences and services.
Beware of Recruitment Fraud: Groupon follows a merit\-based recruitment process without charging job seekers any fees. We've noticed an increase in recruitment fraud, including fake job postings and fraudulent interviews and job offers aimed at stealing personal information or money. Be cautious of individuals falsely representing Groupon's Talent Acquisition team with fake job offers. If you encounter any suspicious job offers or interview calls demanding money, recognize these as scams. Groupon is not responsible for losses from such dealings. For legitimate job openings (and a sneak peek into life at Groupon), always check our official career website at Groupon Careers
Salary Context
This $70K-$110K range is below the median 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 Groupon, 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. Entry-level AI roles across all categories have a median of $76,880. This role's midpoint ($90K) sits 46% below the category median. Disclosed range: $70K to $110K.
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.
Groupon AI Hiring
Groupon has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $110K - $300K.
Location Context
AI roles in Chicago pay a median of $202,350 across 310 tracked positions. That's 10% above the national 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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