Senior Research Scientist (P4370)

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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Senior Research Scientist (AI Enablement) P4370

Cincinnati, OH / Chicago, IL

SUMMARY

We are seeking a highly skilled Senior Research Scientist to join our AI Foundation Models team, focusing on the application of the state-of-the-art in large language models (LLMs), small language models (SLMs), and deep learning architectures on retail problems. This role combines cutting-edge research with practical implementation, requiring expertise in model pretraining, fine-tuning, and production-grade code development.

RESPONSIBILITIES

• Research and implement novel architectures for large language models and small language models
• Lead research initiatives in model pretraining methodologies and optimization techniques
• Develop advanced fine-tuning strategies including parameter-efficient methods (LoRA, adapters, etc.)
• Specialize in encoder-only model architectures and their applications
• Create and optimize embedding models for various downstream tasks
• Conduct experiments to improve model efficiency, performance, and scalability
• Write production-quality code for training, inference, and deployment of language models
• Implement distributed training systems for large-scale model development
• Optimize model architectures for computational efficiency and memory usage
• Develop robust evaluation frameworks and benchmarking systems
• Create reusable libraries and tools for the research team
• Collaborate with cross-functional teams including ML engineers, product teams, and infrastructure
• Mentor junior researchers and contribute to team knowledge sharing
• Present research findings at conferences and publish in top-tier venues
• Stay current with latest developments in NLP and deep learning research

QUALIFICATIONS, SKILLS, AND EXPERIENCE

• PhD in Computer Science, Machine Learning, Artificial Intelligence, or related field
• 1-2 years of post-PhD industry or research experience in deep learning
• Strong publication record in relevant conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.)

Required Skills

• Expert proficiency in PyTorch; familiarity with distributed training frameworks (DeepSpeed, FairScale, etc.)
• Hands-on experience with transformer architectures, attention mechanisms, and modern LLM/SLM implementations
• Proven experience in pretraining large models, including data preprocessing, tokenization, and training dynamics
• Deep understanding of supervised fine-tuning, RLHF, instruction tuning, and parameter-efficient methods
• Specialized knowledge in BERT-style architectures, masked language modeling, and encoder-only applications
• Experience developing and optimizing dense retrieval models, sentence embeddings, and multimodal embeddings
• Ability to write clean, efficient, and scalable Python code suitable for production environments
• Knowledge of model quantization, pruning, and other compression techniques
• Experience with evaluation methodologies for language models
• Experience with model versioning, experiment tracking (Weights & Biases, MLflow), and deployment pipelines
• Strong mathematical foundation in optimization, statistics, and linear algebra
• Experience with cloud computing platforms (GCP, Azure) and containerization (Docker, Kubernetes)
• Proficiency in distributed computing and parallel processing
• Excellent problem-solving skills and ability to work independently
• Strong communication skills for presenting complex technical concepts

Preferred Skills

• Experience with multimodal models and cross-modal understanding
• Familiarity with reinforcement learning from human feedback (RLHF)
• Contributions to open-source deep learning projects
• Experience with hardware optimization (GPUs, TPUs) and mixed-precision training
• Background in natural language processing applications and linguistics

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