Applied Scientist, AMXL Worldwide Science

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 Amazons 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 AMXLs 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 teams 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 Amazons heavy bulky business. The teams 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.

- 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 Masters 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

- 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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status disability or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process including support for the interview or onboarding process please visit for more information. If the country/region youre applying in isnt listed please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience qualifications and location. Amazon also offers comprehensive benefits including 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 and parental leave. Learn more about our benefits at WA Bellevue - 136000.00 - 184000.00 USD annually