AI Sales Associate

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.