Applied Scientist II, ML/AI, Fulfillment Planning and Execution Science

Job Overview: Applied Scientist II, ML/AI, Fulfillment Planning and Execution Science

Have you ever wondered how Amazon predicts delivery times and ensures your orders arrive exactly when promised? Have you wondered where all those Amazon semi-trucks on the road are headed? Are you passionate about increasing efficiency and reducing carbon footprint? Does the idea of having worldwide impact on Amazon's multimodal logistics network that includes planes, trucks, and vans sound exciting to you? Are you interested in developing Generative AI solutions using state-of-the-art LLM techniques to revolutionize how Amazon optimizes the fulfillment of millions of customer orders globally with unprecedented scale and precision? If so, then we want to talk with you! Join our team to apply the latest advancements in Generative AI to enhance our capability and speed of decision making.

Fulfillment Planning & Execution (FPX) Science team within SCOT- Fulfillment Optimization owns and operates optimization, machine learning, and simulation systems that continually optimize the fulfillment of millions of products across Amazon’s network in the most cost-effective manner, utilizing large scale optimization, advanced machine learning techniques, big data technologies, and scalable distributed software on the cloud that automates and optimizes inventory and shipments to customers under the uncertainty of demand, pricing, and supply. The team has embarked on its Generative AI to build the next-generation AI agents and LLM frameworks to promote efficiency and improve productivity.

We’re looking for a passionate, results-oriented, and inventive machine learning scientist who can design, build, and improve models for our outbound transportation planning systems. You will work closely with our product managers and software engineers to disambiguate complex supply chain problems and create ML / AI solutions to solve those problems at scale. You will work independently in an ambiguous environment while collaborating with cross-functional teams to drive forward innovation in the Generative AI space.

Key Job Responsibilities

• Design, develop, and evaluate tailored ML/AI models for solving complex business problems.
• Research and apply the latest ML / AI techniques and best practices from both academia and industry.
• Identify and implement novel Generative AI use cases to deliver value.
• Design and implement Generative AI and LLM solutions to accelerate development and provide intuitive explainability of complex science models.
• Develop and implement frameworks for evaluation, validation, and benchmarking AI agents and LLM frameworks.
• Think about customers and how to improve the customer delivery experience.
• Use analytical techniques to create scalable solutions for business problems.
• Work closely with software engineering teams to build model implementations and integrate successful models and algorithms in production systems at large scale.
• Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation.

A Day in the Life

You will have the opportunity to learn how Amazon plans for and executes within its logistics network including Fulfillment Centers, Sort Centers, and Delivery Stations. In this role, you will design and develop Machine Learning / AI models with significant scope, impact, and high visibility. You will focus on designing, developing, and deploying Generative AI solutions at scale that will improve efficiency, increase productivity, accelerate development, automate manual tasks, and deliver value to our internal customers. Your solutions will impact business segments worth many-billions-of-dollars and geographies spanning multiple countries and markets. From day one, you will be working with bar raising scientists, engineers, and designers. You will also collaborate with the broader science community in Amazon to broaden the horizon of your work. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs. We look for individuals who know how to deliver results and show a desire to develop themselves, their colleagues, and their career.

About The Team

FPX Science tackles some of the most mathematically complex challenges in transportation planning and execution space to improve Amazon's operational efficiency worldwide at a scale that is unique to Amazon. We own the long-term and intermediate-term planning of Amazon’s global fulfillment centers and transportation network as well as the short-term network planning and execution that determines the optimal flow of customer orders through Amazon fulfillment network. FPX science team is a group of scientists with different technical backgrounds including Machine Learning and Operations Research, who will collaborate closely with you on your projects. Our team directly supports multiple functional areas across SCOT - Fulfillment Optimization and the research needs of the corresponding product and engineering teams. We disambiguate complex supply chain problems and create innovative data-driven solutions to solve those problems at scale with a mix of science-based techniques including Operations Research, Simulation, Machine Learning, and AI to tackle some of our biggest technical challenges. In addition, we are incorporating the latest advances in Generative AI and LLM techniques in how we design, develop, enhance, and interpret the results of these science models.

Basic Qualifications

• 3+ years of building models for business application experience.
• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
• Experience in patents or publications at top-tier peer-reviewed conferences or journals.
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.
• Experience building machine learning models or developing algorithms for business application.
• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning.

Preferred Qualifications

• Experience using Unix/Linux.
• Experience in professional software development.
• Experience with popular deep learning frameworks such as MxNet and Tensor Flow.

Key skills/competency

• Machine Learning
• Generative AI
• Large Language Models (LLM)
• Supply Chain Optimization
• Operations Research
• Data Analysis
• Algorithm Development
• Deep Learning
• Distributed Computing
• Model Deployment