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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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LEAD AI/ML ENGINEER (P4645\)
SUMMARY
As a Lead AI/ML Engineer (G3\) on the Labs team, you will serve as a hands\-on technical lead at the intersection of classical optimization science and modern AI/ML. Our Labs team has a strategic focus on building AI\-enhanced optimization systems, designing AI layers that augment, accelerate, and extend classical optimization engines to unlock solutions that neither approach achieves alone. This is not a generalist ML role: you will bring deep optimization foundations and use them as the platform on which the next generation of intelligent, adaptive systems are built.
You will contribute code daily, serve as one of the team's primary subject\-matter experts on optimization formulations, solver selection, and hybrid architecture design, mentor engineers and researchers, and partner with cross\-functional stakeholders to define, deliver, and scale production systems across Kroger.
RESPONSIBILITIES
Serve as a hands\-on developer responsible for building and maintaining end\-to\-end ML, AI, and optimization\-based solutions
Design and build hybrid AI/optimization systems including ML\-guided search, learned warm starts, neural network surrogate models, and AI\-augmented constraint formulations that improve the performance, scalability,interpretability and adaptability of classical optimization solvers
Lead technical design, implementation, and review processes for POCs and production\-ready systems
Lead end\-to\-end solution lifecycle—from rapid prototyping through to scaling and hand\-off to production teams in partnership with other data scientists and engineers within Labs and across the business
Serve as one of the team's primary technical resources on optimization problem formulations, solver selection, and performance benchmarking across constraint types and problem scales
Partner with researchers and data scientists to co\-develop, scale, and operationalize new algorithms
Architect and implement robust ML(AI)Ops pipelines that support experimentation, deployment, and monitoring
Build reusable ML components and APIs that enable modularity and scalability across business areas
Evaluate and adopt emerging technologies and tooling that can enhance experimentation and delivery speed
Drive technical best practices in code quality, documentation, observability, and team knowledge sharing
Drive experimentation and benchmarking to select performant solutions that balance complexity and business value
Contribute to Labs’ collaborative, research\-forward culture by learning, sharing, and mentoring both junior and senior engineers and researchers on industry\-leading and cutting\-edge technologies
Lead and participate in code reviews and technical architecture planning to ensure adherence to preferred patterns and standards
Represent Labs in technical forums; proactively mentor junior and peer engineers
Collaborate with product and business stakeholders to align technical execution with innovation goals
REQUIRED QUALIFICATIONS
Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related field
4\+ years experience experience developing ML, AI, or optimization systems, including production deployment and scaling
Strong software engineering fundamentals and daily coding experience in Python
Deep proficiency in Python and fluency in NumPy, pandas, PySpark and at least 3 of the following MLand Optimization libraries \- PyTorch, TensorFlow, scikit\-learn, and Pyomo (Pyomo proficiency is specifically required).
Hands\-on experience architecting and productionizing at least one type of optimization problem (e.g., network optimization, vehicle routing, scheduling, facility location, or resource allocation).
Practical experience with at least two industry\-standard optimization solvers such as Gurobi, CPLEX, OR\-Tools, Pyomo, PuLP, CBC, or SCIP.
Demonstrated experience designing or prototyping hybrid AI/optimization systems where ML or AI components (surrogate models, learned heuristics, prediction models, AI chatbots) interact with or augment classical optimization solvers
Hands\-on experience designing CI/CD and MLOps workflows using tools such as MLflow, Azure ML, or Databricks
Familiarity with cloud platforms (Azure preferred), containerization (Docker), and orchestration (Kubernetes)
Experience with modern software development practices including testing, logging, observability, and version control
Ability to lead projects through ambiguity and collaborate in highly cross\-functional teams
PREFERRED EXPERIENCE
Deep knowledge of operations research fundamentals such as linear programming, integer programming, mixed\-integer programming, constraint programming, stochastic optimization, or combinatorial optimization
Experience integrating reinforcement learning, neural combinatorial optimization, or other ML\-driven approaches with classical solver frameworks (e.g., ML\-guided branching, policy\-based heuristics, or graph neural networks for combinatorial problems)
Familiarity with applied research at the optimization/AI/ML intersection such as learning to optimize, predict\-then\-optimize, end\-to\-end differentiable optimization, algorithm selection via ML, AI assisted optimization.
Strong track record of partnering with researchers to translate early\-stage ML ideas into deployable systems
Experience prototyping and scaling AI solutions in applied environments
Experience designing experiment platforms or reusable ML/optimization infrastructure
Demonstrated leadership in evaluating trade\-offs between performance, complexity, and maintainability
Familiarity with real\-time or batch data processing systems
Leadership in navigating trade\-offs between performance, complexity, and long\-term maintainability
\#LI\-SSS
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/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At 84.51°, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($166K) sits 24% 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/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 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).
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 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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