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Description

Inventory Planning and Control (IPC) science is seeking a passionate machine learning scientist working in one of its team locations (NYC, Austin or Bellevue) to build the next generation AI-driven decision making systems. IPC owns the core decision models in the space of Buying, Placement, Capacity Control and Planning. Our models decide when, where, and how much we should buy, flow, and hold inventories in our global fulfillment network to meet Amazon’s business goals and to make our customers happy. We do this for hundreds of millions of items and hundreds of product lines worth billions of dollars of world-wide for both our retail and selling partner business. Our systems are built entirely in-house and operate at various scales, from real-time decision system that completes thousands of transactions per seconds, to large scale distributed system that optimizes the inventory decisions over millions of products simultaneously. IPC is also unique in that we are simultaneously developing the science and software of inventory optimization and solving some of the hardest computational/operational challenges in production. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working with some of the best product managers, scientists, and software engineers in the industry.

Key job responsibilities

This particular role focuses on building and experimenting the cutting edge technologies in deep learning and reinforcement learning to decide the inventory flows across Amazon's global fulfillment network for hundreds of millions of different products. This role requires superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. To support their proposals, candidates should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. 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.

A day in the life

IPC science is at the center of Amazon’s supply chain. In this role, you will have the opportunity to work with partners and stakeholders from Amazon’s retail, selling partner and operation departments worldwide. You will understand their challenges and pain points, and help develop solutions that improve how Amazon manages inventory in our global fulfillment network. To implement your solutions, you will work closely with our in-house product and engineering teams. Your work will have high visibility and impacts to Amazon’s business operation.

About the team

IPC science team contains a large group of scientists with different technical backgrounds, who will collaborate closely with you on your projects. Our team directly supports 8 functional areas, developing and maintaining various decision optimization and prediction models behind the scene. We promote experimentation and learn by building. We often seek the opportunity of applying hybrid techniques in the space of Operations Research and Machine Learning to tackle some of our biggest technical challenges.

Basic Qualifications

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

• Experience programming in Java, C++, Python or related language

• Experience in building machine learning models for business application

• 1+ years of practical machine learning experience

• 1+ years of hands-on predictive modeling and large data analysis experience

Preferred Qualifications

• Experience in professional software development

• Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

• Experience with popular deep learning frameworks such as MxNet and Tensor Flow

• Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF

• Experience in patents or publications at top-tier peer-reviewed conferences or journals

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $222,200/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.