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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.
Location \& Work Model: US\-based \- Chicago (hybrid) or remote New York, Miami, Austin, or Boston
### About the Role:
Groupon is rebuilding how it sells. This role is the Foundry bet for Supply — the most strategically important AI build in the commercial organisation. Reporting directly to the CSO, the Head of AI Sales builds and runs a production\-grade fleet of AI agents across two layers. The first layer operates autonomously in parallel to the human sales team — running full sales motions independently: prospecting, outreach, qualification, reactivation, and inbound triage. The second layer augments human reps at the point agents reach their limits: when a rep steps in, they arrive fully equipped — account context surfaced, deal history logged, next steps prepared — and the agents continue working in the background. The goal is a 10x sales organisation: reps move from player to coach, focusing on judgment, relationships, and decisions while agents handle execution. What makes this build uniquely defensible is the data: proprietary consumer transaction signals, merchant health intelligence, and three years of behavioural sales call data that no competitor can replicate. You own both layers, the handoff between them, and the architecture that makes the whole system run. This role does not duplicate what existing functions own — it gives Groupon a parallel sales force, and makes the existing one sharper.
North Star
A two\-layer AI sales system: agents that run full motions autonomously in parallel to the human team, and agents that make human reps sharper the moment they step in. Reps close deals and manage relationships. Agents do everything else — before, during, and after. And the system gets better over time: every signal, every call, every conversion feeds back into the fleet. Scale compounds. Investment does not.
What You'll Do:
- Build and own the agent architecture — Design the full orchestration layer: lead scraping and ICP\-scoring, BD outbound sequencing, IB triage and routing, MD reactivation, and multi\-touch National/Enterprise engagement — all running in parallel, production\-grade from day one.
- Run and iterate the fleet on live data — Own in\-production performance. Define signal logic, trigger conditions, prompt architecture, and account prioritisation intelligence (PDS score, pyramid, campaign tags, open CRM cases). Build and operate the call intelligence layer: process call transcripts at scale, score performance, extract competitive patterns, and surface category playbooks and coaching signals to reps and managers. Test on live merchant data, kill what doesn't convert, ship before you're comfortable.
- Own both layers — autonomous motions and human augmentation — Layer 1: agents run complete workflows without rep involvement — prospect identification, outreach sequences, lead qualification, reactivation, and inbound first response. Reps receive warm, qualified opportunities, not cold lists. Layer 2: where an agent reaches its limit and a human steps in, the rep arrives equipped — account context surfaced, deal history logged, suggested approach ready. Agents continue to work in the background while the rep handles what requires judgment. You design both layers and the handoff between them.
- Coordinate — don't duplicate — Align with internal stakeholders across Sales, Commercial, and LOB leadership on what the fleet should solve next. You set the AI architecture; they deliver within it. Every request from the commercial organisation becomes a roadmap decision, not a distraction.
- Own the vendor stack — Evaluate, deploy, and exit. Know where 11x.ai, Artisan, Clay, Amplemarket, and Dust break at scale. Clear build\-vs\-buy framework, no expensive lock\-in.
What You Bring:
- Top\-tier academic background or equivalent formation in a high\-calibre AI or technology environment — keeping the door open to exceptional self\-starters who came up through companies like OpenAI, Palantir, or a serious AI startup rather than a traditional degree path.
- 7\+ years at the intersection of AI and commercial/sales — enterprise AI SaaS, GenAI, or AI solutions. Builder track record required.
- Shipped multi\-agent sales automation in production: sequencing, routing, CRM automation, AI dialers, call analysis, or reactivation.
- Full\-stack fluency at Level 8\+ agentic maturity: orchestrator \+ specialist agent architecture, parallel sessions, background cron agents, autonomous loops. LLM APIs (OpenAI, Anthropic), LangChain/Dust/CrewAI, Clay/Apollo/ZoomInfo, Python, Salesforce Opportunity\-level flows.
- Deployed AI SDR platforms (11x.ai, Artisan, Amplemarket, or equivalent) — you know where they break and have a clear build\-vs\-buy framework.
- Commercial judgment across sales motions — you know what an agent can own end\-to\-end vs. where a human must take over, and you design the handoff intelligently. Every motion tied to a measurable ROI proof point.
Who You Are:
- Builder first — you build before you ask for help, and fix before you escalate.
- AI\-native, not AI\-curious — you design agents that own full workflows autonomously. Humans are not in the loop until the moment they need to be.
- Moves in days — you test in production, iterate on live merchant data, and ship before you're comfortable.
- Commercially sharp — GP growth is the goal; technology is the means. You kill what doesn't convert.
How We Operate:
Five principles. Non\-negotiable.
- Extreme Ownership — you own every agent's output — not the vendor, not the backlog.
- Speed Over Comfort — ship before perfect, test live, kill what doesn't convert.
- Impact Obsessed — every initiative shows up in a P\&L metric within 30 days or it doesn't stay.
- Simplify to Scale — every vendor earns its place; complexity kills adoption.
- Disciplined — cost\-efficient, auditable, maintainable stack decisions — built to last.
How We Measure Your Success:
- Pipeline velocity: \+15–20% vs. pre\-deployment baseline — with Enterprise/National improvement as the primary commercial target
- Meetings booked per rep: \+25–40% via AI\-assisted outbound and inbound
- GP contribution from AI\-driven acquisition, reactivation, and expansion
- Time\-to\-proven\-ROI per agent deployment: measurable within 30 days of go\-live
- Stack simplicity: fewer systems, fewer handoffs, fewer manual steps per LOB — tracked quarterly (complexity is a failure mode)
- Rep hours saved per week on repeatable, low\-judgment tasks — tracked by LOB
- Agent adoption rate: % of target reps using AI\-assisted tooling daily within 30 days of each deployment — tracked per LOB
#### The Details:
- Location: Downtown Chicago (hybrid, 3 days a week in\-office)
+ Alternate locations: Remote for New York, Miami, Austin, or Boston
- Salary Range:$200,000 – $300,000, 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 $200K-$300K range is above the 75th percentile 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($250K) sits 50% above the category median. Disclosed range: $200K to $300K.
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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