Seattle, WA, US
17 hours ago
Aerodynamic Modeling Research Scientist, Prime Air Flight Sciences Aerodynamic Modeling
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How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog (https://www.aboutamazon.com/news/transportation/amazon-prime-air-delivery-drone-reveal-photos).

If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you.

Come work on the Amazon Prime Air Team!


Our Prime Air Drone Flight Sciences High Fidelity Methods (HFM) team is looking for an outstanding member to develop and verify our Aerodynamic Database and associated aerodynamics models used for engineering analyses and vehicle simulations. These models are the backbone of every flight simulation performed within Prime Air and are a critical element in the aircraft design, verification, and certification process.

These models are used to predict many attributes of the vehicle performance including range, maneuverability, tracking error, and aircraft stability. They are a key input to design decisions, vehicle component sizing and flight software algorithm development. The accuracy and reliability of these flight model are critical to the success of Prime Air.

Key job responsibilities
The person in this role is responsible for owning the development, deployment, verification, and maintenance of models from end-to-end. This includes the initial gathering of the downstream customer needs, identifying the most suitable modelling approach, coordinating the generation of input data, training models, developing and maintaining software interfaces, and verifying the model accuracy.

This person will also be responsible for determining the most suitable modeling approach for a given physical phenomena. They need to possess knowledge of various machine learning techniques, and their respective advantages and limitations. They will need to have a detailed understanding of the types of physics to be modelled including vehicle aerodynamics, multibody dynamics, and atmosphere physics.

They will be responsible for designing experiments for generating data used to train and verify surrogate models. They need to have a basic understanding of the methods used to generate high-fidelity aerodynamics predictions including CFD, wind tunnel testing, and flight testing. They will be responsible for validating the models by leveraging uncertainty quantification, system identification, and statistical analyses.
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