Senior Applied Scientist, C360

Amazon • Seattle, WA

2d • $167,100 - $226,100 • Onsite

About The Position

We are seeking scientists passionate about advancing Information Retrieval, NLP, and Large Language Models to join our team at Amazon. This role offers access to massive datasets, world-class compute, and a team of top scientists and engineers building the future of e-commerce. Our team develops large-scale ML solutions that provide personalized, up-to-date recommendations to millions of customers, shaping the customer shopping journey. We are looking for scientists with deep LLM expertise to build our next generation of models, focusing on post-training techniques such as instruction tuning, reward modeling, reinforcement learning, and multi-modal alignment. You will design and execute large-scale experiments, analyze model behavior, and develop training methodologies to enhance core capabilities like reasoning and personalization.

Requirements

• 4+ years of applied research experience
• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Nice To Haves

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.

Responsibilities

• Own the scientific roadmap for personalization initiatives, identifying high-impact research directions and translating ambiguous business problems into well-defined ML formulations
• Design and lead end-to-end systems spanning recommendations, information retrieval, and LLM fine-tuning, from problem framing through offline experimentation to production A/B testing and launch
• Drive technical decisions on model architecture, training methodology, and evaluation frameworks, balancing scientific rigor with business impact and operational constraints
• Mentor and raise the bar for the science team through design reviews, paper discussions, and establishing best practices for experimentation and reproducibility
• Influence cross-functional strategy by partnering with engineering, product, and leadership to define the product vision informed by what's technically feasible and scientifically novel
• Publish and advance the state of the art — contribute to the broader ML community through patents, publications, and external engagement at conferences

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave
• sign-on payments
• restricted stock units (RSUs)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior