About the job
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.
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
Qualifications
Minimum
Experience programming or scripting language like Python, Java, C or C++
Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse
Currently has, or is in the process of obtaining, a Master's degree or above in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
Experience building machine learning models or developing algorithms for business application
Preferred
Currently has, or is in the process of obtaining, a PhD in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields
Have publications at top-tier peer-reviewed conferences or journals
Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning
Experience in designing experiments and statistical analysis of results
Experience in solving business problems through machine learning, data mining and statistical algorithms