Do you want to join an innovative team applying machine learning, advanced optimization techniques, and Large Language Models (LLMs) to transform the delivery of heavy and bulky items for Amazon customers? Are you excited about working with large-scale operational data and developing models that solve real-world logistics and fulfillment challenges? If so, the Amazon Extra Large (AMXL) Science team may be the right fit for you. AMXL is Amazon's specialized business for delivering heavy and bulky items, including appliances, furniture, fitness equipment, and mattresses, with a premium customer experience that includes room-of-choice delivery, at-home installations, and assembly services. We are seeking an Applied Scientist to help develop scalable machine learning and optimization solutions that improve delivery efficiency, capacity planning, network design, and customer experience across our rapidly growing network. In this role, you will partner with senior scientists and engineers to translate complex operational problems into data-driven solutions, build and evaluate models, and contribute to next-generation fulfillment and logistics systems. Key job responsibilities Apply machine learning, statistical techniques, time series modeling, and operations research to build and improve models for delivery routing, capacity planning, demand forecasting, workforce scheduling, and network optimization Analyze large-scale historical and real-time operational data to identify efficiency patterns, bottlenecks, and emerging trends across the AMXL network Develop, validate, and deploy innovative models under the guidance of senior scientists to improve cost-to-serve and customer experience Experiment with emerging technologies, including Generative AI and LLMs, to enhance automation, scheduling, and operational decision-making Collaborate closely with software engineers to implement models in real-time production systems Partner with operations, product, and business teams to translate operational insights into actionable improvements Build scalable, automated pipelines for data analysis, model training, and validation Monitor model performance and provide clear reporting on key operational and business metrics Research and prototype new modeling approaches to improve system performance and delivery quality A day in the life You will be working within a dynamic, diverse, and supportive group of scientists who share your passion for innovation and excellence in logistics and fulfillment science. You will work closely with business partners, operations teams, and engineering teams to create end-to-end scalable machine learning solutions that address real-world challenges across AMXL's heavy and bulky delivery network, including demand forecasting, capacity planning, routing optimization, and customer experience improvement. You will build scalable, efficient, and automated processes for large-scale data analyses, model development, model validation, and model implementation in production systems. You will also provide clear and compelling reports on your solutions to both technical and non-technical stakeholders, and contribute to the ongoing innovation and knowledge-sharing that are central to the team's success. About the team The AMXL (Amazon Extra Large) Worldwide Science team is a multidisciplinary organization of data scientists, applied scientists, and product managers dedicated to solving some of the most complex supply chain and logistics challenges in Amazon's heavy bulky business. The team's mission is to leverage advanced analytics, machine learning, and optimization science to drive measurable improvements across the AMXL end-to-end supply chain — from inbound fulfillment and middle-mile transportation to last-mile delivery of heavy and bulky items. The science team transforms complex operational data into actionable intelligence that directly impacts customer experience, cost efficiency, and delivery performance at a worldwide scale.
August 29, 2019An automatic-speech-recognition system — such as Alexa’s — converts speech into text, and one of its key components is its language model. Given a sequence of words, the language model computes the probability that any given word is the next one. For instance, a language model would predict that a sentence that begins “Toni Morrison won the Nobel” is more likely to conclude “Prize” than “dries”. Language models can thus help decide between competing interpretations of the same acoustic information.
