Senior Software Development Engineer , Stores Foundational AI - Rufus

Requirements

• 5+ years of non-internship professional software development experience
• 5+ years of programming with at least one software programming language experience
• 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
• Experience as a mentor, tech lead or leading an engineering team
• Experience with vLLM, SGLang, TensorRT or similar platforms in production environments
• Experience with CUDA kernels or ML/low-level kernels
• 5+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Bachelor's degree in computer science or equivalent
• Experience with Machine Learning and Large Language Model fundamentals, including architecture, training/inference lifecycles, and optimization of model execution
• Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
• USA, CA, Palo Alto - 193,300.00 - 261,500.00 USD annually
• USA

Additional Information

We are building foundational LLMs for Amazon Stores that fuse world knowledge with deep e-commerce understanding to power next-generation shopping experiences. These systems continuously learn from real-world customer interactions to become more helpful, personalized, and context-aware over time. We are looking for builders who are passionate about large-scale systems, AI innovation, and customer impact. You will work at the intersection of distributed systems, machine learning infrastructure, and science to bring frontier research-especially in post-training and reinforcement learning-into production at Amazon scale. Key job responsibilities * Architect and build scalable ML infrastructure powering LLM training and post-training workflows, including supervised fine-tuning, reinforcement learning, and continuous learning from live traffic * Transform real-world customer interactions into high-quality training signals, enabling continuous model improvement and better customer experiences * Build and optimize post-training and RL systems, including reward modeling, policy optimization, data collection loops. * Drive experimentation and iteration velocity by building tooling and frameworks that enable rapid hypothesis testing, signal validation, and model quality improvements * Partner closely with applied scientists to translate frontier techniques (e.g., RLHF, agentic workflows, multi-turn optimization) into reliable, production-grade systems * Own systems end-to-end, including design, implementation, deployment, observability, and operational excellence * Raise the engineering bar through technical leadership, design reviews, and mentorship, influencing best practices across the organization

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