Senior AI/ML Engineer - Research (P4368)

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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Senior AI/ML Engineer P4368)

We are seeking a highly skilled Senior AI/ML Engineer to join our AI Foundation Models team, focused on pushing the boundaries of AI capabilities through advanced reasoning, multi-step problem solving, and deep agentic systems. In this role, you will drive experimentation and rapid iteration across LLMs, deep learning, reinforcement learning, world modeling, and causal reasoning to solve complex retail problems at scale. You will own the full lifecycle of these systems—from prototyping and experimentation through production-grade deployment, optimization, and monitoring.

Responsibilities

• 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

Required Skills

• Hands-on experience building agentic systems, multi-step reasoning systems, or research agents using LLMs, with strong understanding of agentic design patterns including planning, tool use, memory, RAG, and self-reflection
• Proven experience with RLHF, RLEF, reward modeling, PPO, DPO, or related alignment and RL techniques
• Familiarity with world modeling, causal inference, and causal reasoning frameworks applied to decision-making and planning
• Expert proficiency in PyTorch with deep experience in transformer architectures and modern LLM/SLM implementations; familiarity with distributed training frameworks (DeepSpeed, FairScale, Megatron)
• Experience in pretraining large models including data preprocessing, tokenization, training dynamics, fine-tuning, and parameter-efficient methods
• Knowledge of encoder-only architectures, masked language modeling, embedding models, and dense retrieval
• Clean, efficient, scalable Python coding skills with knowledge of model quantization, pruning, distillation, and compression techniques
• Experience with experiment tracking (MLflow), deployment pipelines, cloud platforms (GCP, Azure), and containerization (Docker, Kubernetes)
• Strong problem-solving skills, ability to work independently on ambiguous problems, and excellent communication skills

Preferred Skills

• 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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