Interested in this AI/ML Engineer role at Groupon?
Apply Now →Skills & Technologies
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.
Role Purpose:
The Sr. Manager of Sales Tooling \& AI is a strategic and technical leader responsible for architecting, optimizing, and scaling Groupon's sales technology ecosystem, with primary ownership of Salesforce and Salesloft. This role sits at the intersection of Sales, Sales Operations, Engineering, and Analytics, ensuring our sellers are equipped with intuitive, automated, and AI\-enabled tools that drive productivity and performance.
This leader will modernize and simplify a historically layered sales tech stack by establishing a clear point of view on systems architecture, automation strategy, and AI\-forward solutioning. The mandate is not simply to administer tools, but to transform them into a competitive advantage by reducing friction, improving data integrity, and enabling intelligent workflows that drive measurable commercial outcomes.
The Sr. Manager will lead a high\-performing team of Salesforce Administrators and Business Program Managers responsible for CRM governance, sales engagement optimization, automation, integrations, and continuous innovation across the sales technology landscape.
Key Responsibilities:
#### Sales Technology Strategy \& Architecture
- Define and own the strategic roadmap for Salesforce, Salesloft, and the broader sales tech ecosystem.
- Establish scalable architecture standards, governance models, and integration frameworks.
- Partner with Sales Leadership and Sales Ops to translate business priorities into system capabilities and workflow design.
- Rationalize legacy tools and technical debt to simplify the seller experience.
#### Salesforce Ownership \& Optimization
- Serve as the executive owner of Salesforce strategy, configuration, data model integrity, and user experience in conjunction with the Engineering department.
- Lead enhancements across pipeline management, forecasting, account hierarchy, territory alignment, and reporting.
- Drive automation of manual workflows and process improvements.
- Ensure data governance standards and scalable object architecture that supports evolving business models.
#### Salesloft \& Sales Engagement Excellence
- Own the strategy, implementation, and optimization of Salesloft.
- Design structured cadence frameworks aligned to funnel strategy and segmentation.
- Improve seller adoption through workflow integration with Salesforce and enablement best practices.
- Leverage Salesloft analytics to continuously refine outreach effectiveness and pipeline generation.
#### Automation \& AI Forward Solutioning
- Identify and implement automation opportunities that eliminate low\-value administrative work for sellers.
- Partner with Data \& Analytics to embed AI\-driven insights into daily sales workflows (e.g., next\-best\-action, prioritization, lead scoring).
- Evaluate and pilot emerging AI tools that improve seller productivity, forecasting accuracy, and engagement quality.
- Ensure AI initiatives are practical, measurable, and integrated into core operating processes.
#### Reporting, Insights \& Performance Enablement
- Collaborate with Analytics to define the core sales data model and system\-driven metrics.
- Enable real\-time visibility into pipeline health, activity effectiveness, and productivity drivers.
- Ensure tools reinforce desired sales behaviors and align with incentive design and performance frameworks.
#### Change Management \& Adoption
- Lead change management for new tools, automations, and system enhancements.
- Drive high adoption through structured communication, training, and feedback loops.
- Establish clear SLAs, intake processes, and prioritization frameworks for system requests.
#### Team Leadership \& Cross\-Functional Partnership
- Build and develop a high\-performing Sales Tooling team, including Salesforce administrators, system analysts, and automation specialists.
- Act as a trusted partner to Sales Leadership, Marketing, Engineering, Finance, and Data teams.
- Balance speed and innovation with governance, compliance, and long\-term scalability.
Qualifications:
- 8–12\+ years of experience in Sales Operations, Revenue Operations, Sales Technology, Engineering, or related roles in mid\-to\-large enterprise environments.
- 5\+ years of deep, hands\-on Salesforce experience, including architecture, automation (Flows), integrations, and data governance. Salesforce Admin and/or Advanced Admin certification strongly preferred.
- Strong expertise in Salesloft (or comparable sales engagement platform) with demonstrated success driving adoption and measurable productivity gains.
- Proven experience designing and implementing automation strategies that improve sales efficiency.
- Exposure to AI\-enabled sales tools, predictive modeling, or workflow intelligence solutions.
- Experience leading and developing technical or systems\-focused teams.
- Strong stakeholder management skills in matrixed, cross\-functional environments.
- Highly analytical, systems\-oriented thinker with the ability to connect tooling decisions to commercial outcomes.
We Are Looking For:
- Systems Architect: Thinks in terms of scalable design, clean data models, and long\-term sustainability.
- Product Mindset Operator: Treats Salesforce and Salesloft as evolving products, not static tools.
- Automation Champion: Obsessed with eliminating manual effort and increasing seller focus on revenue\-generating activity.
- AI Pragmatist: Excited about AI innovation but disciplined in applying it where it drives measurable impact.
- Adoption Driver: Understands that a tool only matters if sellers use it effectively and consistently.
How We Measure Your Success:
First 2\-3 Months
- Clear, documented sales tooling roadmap aligned to commercial priorities.
- Improved Salesforce data hygiene and workflow efficiency.
- Increased Salesloft adoption and measurable improvements in activity\-to\-pipeline conversion.
- Reduction in manual seller administrative time through automation initiatives.
3–6 Months
- Embedded AI\-driven workflows that improve prioritization, forecasting accuracy, and outreach effectiveness.
- Streamlined tech stack with reduced redundancy and improved integration stability.
- Measurable gains in seller productivity and pipeline velocity attributable to tooling enhancements.
Longer Term
- A sales technology ecosystem that is intuitive, scalable, and AI\-enabled.
- Sellers spending more time selling and less time navigating systems.
- Sales Tooling recognized as a strategic lever for revenue growth and operational excellence.
Why You'll Love Working Here:
- High\-impact role with visibility to senior leadership – Help shape the systems that power Groupon's global sales organization.
- Opportunity to influence the future of a global public company – Play a key role in modernizing the technology and workflows that drive our commercial growth.
- Entrepreneurial environment where your contributions matter – Move quickly, simplify complexity, and see your work translate into real business impact.
- A culture that values curiosity, ownership, and results – We empower people who take initiative, challenge the status quo, and deliver outcomes.
#### The Details:
- Location: Downtown Chicago (hybrid, 3 days a week in\-office)
+ Alternate location: Open to candidates living in València, Spain
- Salary Range for Chicago:$120,000 – $150,000, plus eligibility to participate in a performance\-based bonus program.
+ Estimated Total OTE: $150,000 – $187,500\+
- 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 $120K-$150K range is above 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($135K) sits 19% below the category median. Disclosed range: $120K to $150K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.