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Applied Scientist - Simulation, Last Mile Delivery
- Amazon (Bellevue, WA)
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Description
Join Amazon's Next-Generation Delivery Technology Team!
Are you passionate about pushing the boundaries of simulation technology and applied research? We're seeking innovative Applied Scientists to join our team in developing advanced simulation solutions that will revolutionize the future of logistics!
The Last Mile Technology organization is pioneering sophisticated simulation and modeling solutions that are transforming Amazon Logistics (AMZL). Our mission is to create high-fidelity digital environments and intelligent systems that enable rapid testing and validation of next-generation delivery solutions while maintaining Amazon's highest standards for safety and reliability.
As an Applied Scientist on our Simulation team, you'll be at the intersection of research and practical applications. You'll develop advanced simulation frameworks that model complex real-world scenarios, implement sophisticated physics-based models, and create digital twins that enable rapid testing and validation of delivery systems.
What Makes This Role Exciting:
- Research Impact: Transform theoretical concepts into practical solutions that shape the future of delivery technology
- Complex Problem Solving: Work on challenging physics-based simulations, environmental modeling, and scenario generation
- State of the Art Tech Stack: Leverage advanced simulation frameworks, machine learning, and high-performance computing
- Innovation at Scale: Develop novel approaches to simulation and validation of complex systems
- End-to-End Research: Drive projects from concept to production-ready solutions
Basic Qualifications
- 3+ years of building models for business application experience
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience in patents or publications at top-tier peer-reviewed conferences or journals
- Experience programming in Java, C++, Python or related language
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
Preferred Qualifications
- * Experience in building and launching deep learning and machine learning models for business applications
- * Solid knowledge of big data and cloud technologies (e.g., Spark, AWS, etc.)
- * Experience with information retrieval, recommender systems, natural language processing, and/or personalization algorithms
- * Publications at top Web, Machine Learning, Natural Language Processing conferences such as KDD, ICML, NeurIPS, ACL, EMNLP, etc.
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 https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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 $223,400/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.
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