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.

Join us at 84.51°!

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Lead AI/ML/Optimization Engineer (G3) – Labs Innovation Focus (P3227)

SUMMARY
As a Senior AI/ML Engineer (G3) on the Labs team, you will serve as a hands-on technical lead responsible for both implementing robust code and guiding the architectural direction of ML/AI/optimization-based systems. This role blends deep engineering expertise, applied ML and optimization research, and system design to accelerate the transition from proof-of-concept to scalable business solution. You will contribute code daily, mentor junior engineers, and collaborate with cross-functional partners to define, deliver, and scale the next generation of AI/ML/optimization capabilities across Kroger.

RESPONSIBILITIES

• Serve as a hands-on developer responsible for building and maintaining end-to-end ML, AI, and optimization-based solutions
• 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
• 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.
• 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 one industry-standard optimization solver such as Gurobi, CPLEX, OR-Tools, Pyomo, PuLP, CBC, or SCIP.
• 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

• 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

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