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About This Role
84\.51° Overview:
84\.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.
Powered by cutting\-edge science, we utilize first\-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer\-centric journey using 84\.51° Insights, 84\.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.
*84\.51° follows a 5‑day in‑office work schedule to support collaboration, alignment, and team connection.*
Join us at 84\.51°!
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The AI Enablement team builds foundational AI capabilities that transform Kroger and 84\.51, including better solutions and outcomes for our shoppers and our associates. AI Gateway is a cross\-functional team within AI Enablement responsible for the primary AI infrastructure that powers these solutions and democratizes access to AI models. As a Product Owner for this team, you will work closely with the AI Gateway Product Director, Tech Leads for each product, and cross\-functional design and development teams.
The LEAD PRODUCT MANAGER is the chief navigator, business champion, and team linguist that is accountable for setting team priorities and maximizing the value of the product results from the work of the development team. You own the details of the business and technical value delivered at the team level. You translate business requirements into product requirements to ensure that the product delivers on user needs. You own the measurement of the value delivered and other metrics for your product. This role requires excellent organizational and communication skills, technical acumen, and artificial intelligence domain knowledge.
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United Stated and with the Kroger Family of Companies (i.e. H1\-B visa, F\-1 visa (OPT), TN visa or any other non\-immigrant status).
RESPONSIBILITIES:
- Drive business value for the team by prioritizing and ordering product backlog items based on the product roadmap, inclusive of product, engineering, UX and data science roadmaps.
- Experienced in managing multiple teams' product backlogs in parallel and ensuring they're transparent, visible and clearly communicated.
- Partner with cross\-functional engineering, UX and data science peers to translate business desires/requirements into effective features and stories that are ready for development.
- Outlines clear acceptance criteria, to clearly communicate the definition of "done".
- Partner with external contractors or technical support to ensure the delivery of the scope of work, within the agreed timeline, and in accordance to the stated acceptance criteria.
- Deep understanding of the business domain, stakeholders, business processes and constraints in which your product(s) play
- Deep understanding of customers/users' problems, how they think and operate and the platforms/technology they use. Ability to become familiar with new technology, business domains, and/or analytical practices that impact your product(s).
- Understand and communicate organization objectives, strategies and solution needs to the team to facilitate results (where collaborative/as colleague)
- Manage and communicate dependencies and risks.
- Stakeholder management – experienced in building and managing internal and external stakeholder relationships to influence and drive change.
- Experienced level knowledge in the development and use of product data to influence product decisions/roadmap – both quantitative and qualitative.
- Clearly communicate the value of the work being delivered
QUALIFICATIONS, SKILLS, AND EXPERIENCE:
- Professional work experience in artificial intelligence / Generative AI applications.
- Skilled in managing cross\-functional software development teams in an agile environment
- Skilled in managing business priorities against design/user needs while delivering against product roadmaps
- Skilled in managing end\-to\-end software product development.
- Stakeholder management skills – experienced in building and managing stakeholder relationships to influence and drive change.
- Problem solving, critical thinking and analytical skills
- Planning, organizational and time management skills
- Comfort with ambiguity – remains confident, positive and resourceful in the face of ambiguity and unexpected results; recovers quickly from setbacks.
- Exhibits qualities of and have proven to be a great teammate
- Verbal and written communication skills, ability to translate between business and technical
- Technical acumen – grasps the technical details to lead a team
- 4\-6 years relevant experience
- Education: Bachelor's degree or equivalent experience
ADDITIONAL SKILLS OF INTEREST
- Prior experience as a software engineer or Technical Product Manager – many of our customers are software engineers and data scientists.
- Experience deploying or working on a team that has deployed AI/ML models in cloud\-based settings.
- Experience navigating corporate governance processes including Security, Cloud, Privacy, Data Sharing, and Legal.
- Experience leveraging various Generative AI chat tools (ie M365 Copilot, ChatGPT, Gemini, Claude) to simplify your life, work, or otherwise automate processes and accomplish previously impossible things.
- Experience developing AI Agents using an agent builder or agent framework.
- Experience developing or using MCP servers.
- Experience using coding agents (ie Claude Code, Codex, Gemini CLI, Github Copilot).
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Salary Context
This $125K-$207K range is below the median for AI Product Manager roles in our dataset (median: $188K across 140 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At 84.51°, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($166K) sits 23% below the category median. Disclosed range: $125K to $207K.
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.
84.51° AI Hiring
84.51° has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Cincinnati, OH, US, Chicago, IL, US. Compensation range: $207K - $207K.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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