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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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We’re looking for a strategic, collaborative, and delivery\-minded Applied AI Lead to help bring AI to life across Kroger, KTD and 84\.51°. This role sits at the intersection of AI capability development and business impact, focused on driving AI adoption and application, from consultation to co\-development.
You’ll lead prototyping efforts, drive tangible use case delivery, and serve as a champion for scaling AI Enablement tools and services. This is a high\-visibility, high\-impact role that requires someone who can connect technical potential with business value — and deliver quickly.
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:
The ideal candidate will be responsible for bridging the gap between cutting\-edge, foundational AI technologies and Kroger, KTD, and 84\.51° business strategies. This individual will play a crucial role in understanding business needs, users’ problems and tying them back to the Kroger AI Enablement technical roadmap. This role will ensure AI initiatives and priorities are aligned with business vertical objectives to drive innovation and efficiency. They will help to drive adoption of AI capabilities across the business by being the conduit between the technical teams and the business.
Responsibilities include:
- Partner with business and product teams to identify, co\-develop, and validate high\-impact use cases
- Lead Applied AI MVP and prototype delivery in close collaboration with data scientists, engineers, and UX
- Drive adoption of AI Enablement capabilities, GenAI productivity tools, and reusable components across business teams
- Champion responsible, explainable, and user\-centered AI in all applied solutions
- Lead Super Agent (orchestration) strategy and implementation for Customer\-facing and Supplier\-facing experiences
- Serve as AI representative/lead on high\-value, end\-to\-end initiatives, in a ‘tiger team’ capacity
- Own AI Spoke Lead network (AI advocates named on each business team) through monthly meetings, regular Teams communications, and ongoing check\-ins; enable AI Spoke Leads to lead AI consults on their own teams and surface high\-priority opportunities to our team
- Facilitate and scale AI opportunities through AI ideation workshop sessions
- Track ongoing business value of AI use cases
- Own core product management responsibilities, including OKRs, roadmaps, planning cycles, and day‑to‑day execution in Jira
Cross\-Functional Collaboration
- Domain Product \& Business Teams: Co\-develop use cases, drive adoption, embed AI where it matters
- AI Enablement Team: Shape scalable services, relay feedback, align tooling with real needs
- Data Science \& Engineering: Translate opportunity into prototypes and working solutions
- Vendors/Partners: Guide third\-party development and delivery with strategic alignment
- Change Management \& Enablement: Support onboarding, activation, and success metrics tracking
QUALIFICATIONS, SKILLS, AND EXPERIENCE:
Successful candidates will thrive in a fast\-paced environment that is a little unorthodox at times and will possess the following:
- Minimum Bachelor’s degree
- 7\+ years of experience influencing and driving business strategy, bridging technical and business needs, and driving cross\-functional impact
- Strong understanding of AI/ML and GenAI technologies, and how to apply them to business problems
- Stakeholder management \- proven relationship\-builder with ability to lead cross\-functional initiatives, facilitate workshops, influence and align diverse stakeholder groups to drive change
- Technical liaison \- ability to effectively translate business requirements into clear technical specifications for DSR and Engineering teams
- Organize the chaos \- demonstrated expertise in managing competing priorities across multiple business teams simultaneously, and ability to bring structure and clarity to complex, fast\-moving environments
- Proactive influence \- proactively seeks out opportunities, identifies unmet needs, and drives adoption of innovation solutions rather than waiting for inbound requests
- Executive\-level presence \- proven ability to influence senior stakeholders and navigate organization dynamics to advance strategic initiatives, and challenge existing processes when needed
- Business acumen \- knows the 84\.51 business, and has experience navigating KTD and GO initiatives and knows the cross\-team ways of working
- Process\-oriented \- product management experience or process\-oriented mindset for OKR, cycle planning, and roadmap development
- Intellectually curious and love learning new skills and capabilities, i.e., agile principles and AI technologies
- Value\-driven decision making \- proven track record of tracking business value and making data\-driven prioritization decisions
\#LI\-EB1
Pay Transparency and Benefits
- The stated salary range represents the entire span applicable across all geographic markets from lowest to highest. Actual salary offers will be determined by multiple factors including but not limited to geographic location, relevant experience, knowledge, skills, other job\-related qualifications, and alignment with market data and cost of labor. In addition to salary, this position is also eligible for variable compensation.
- Below is a list of some of the benefits we offer our associates:
- + Health: Medical: with competitive plan designs and support for self\-care, wellness and mental health. Dental: with in\-network and out\-of\-network benefit. Vision: with in\-network and out\-of\-network benefit.
+ Wealth: 401(k) with Roth option and matching contribution. Health Savings Account with matching contribution (requires participation in qualifying medical plan). AD\&D and supplemental insurance options to help ensure additional protection for you.
+ Happiness: Paid time off with flexibility to meet your life needs, including 5 weeks of vacation time, 7 health and wellness days, 3 floating holidays, as well as 6 company\-paid holidays per year. Paid leave for maternity, paternity and family care instances.
Pay Range
$125,000 \- $207,000 USD
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 in Demand for This Role
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.
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