Role 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°!
What you'll work on
- Design, develop, and deploy end-to-end deep reasoning and research agents capable of complex, multi-step problem solving for the retail domain
- Architect agent systems leveraging test-time compute scaling strategies to enhance reasoning quality during inference
- Develop and apply reinforcement learning techniques to improve agent reasoning capabilities and alignment; design reward functions, preference models, and human feedback pipelines for complex reasoning tasks
- Research and experiment with world modeling and causal reasoning approaches to enable agents to understand cause-effect relationships and simulate outcomes
- Lead research in model pretraining, fine-tuning, instruction tuning, and parameter-efficient methods (LoRA, adapters, QLoRA); implement novel architectures and prompting strategies across LLMs and SLMs
- Build and optimize encoder-only architectures, embedding models, and dense retrieval systems for downstream agent capabilities
- Write production-quality, scalable code for training, inference, and deployment; implement distributed training systems optimized for efficiency, memory, and latency
- Develop evaluation frameworks and benchmarking systems for reasoning quality, agent reliability, and task completion
- Collaborate with cross-functional teams including researchers, product teams, and infrastructure; mentor junior engineers
*Qualifications
Education & Experience**
- Masters in Computer Science, Machine Learning, Artificial Intelligence, or related field
- 1-2 years of industry or research experience in deep learning
What we're looking for
- Experience with multimodal models, cross-modal reasoning, and vision-language agents
- Familiarity with agent orchestration tools (LangChain, LangGraph, AutoGen, or custom frameworks)
- Contributions to open-source deep learning or agentic AI projects
- Experience with hardware optimization (GPUs, TPUs), mixed-precision training, and synthetic data generation
- Background in NLP applications, information retrieval, or knowledge-intensive tasks
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Tags & focus areas
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