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Amazon Applied Scientist - Perception, Amazon Robotics in North Reading, Massachusetts

Description

We are seeking an Applied Scientist to develop innovative perception and machine learning solutions for robot workcells in Amazon Fulfillment centers. In this role, you will leverage advanced sensor technologies to develop and implement state-of-the art ML models to for scene understanding and objection localization. Your solutions will allow Amazon to increase productivity and efficiency while prioritizing employee safety.

Key job responsibilities

  • Design and implement advanced machine learning models for perception tasks such as object detection and scene understanding

  • Optimize and deploy the ML models on edge devices

  • Build and test prototype robotic workcell setups to validate the performance of the solution

  • Work with cross-functional teams to provide inputs and recommendations for the optimal sensor suite to enable a robust solution

  • Collaborate with Amazon's robotics engineering and operations teams to understand their requirements and develop tailored solutions

  • Document the architecture, performance, and validation of the final system

A day in the life

Amazon offers a full range of benefits for you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include:

  1. Medical, Dental, and Vision Coverage

  2. Maternity and Parental Leave Options

  3. Paid Time Off (PTO)

  4. 401(k) Plan

If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!

Basic Qualifications

  • PhD, or Master's degree and 4+ years of science, technology, engineering or related field experience

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

  • 3+ years of building machine learning models for business application experience

  • 2+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience

  • Experience applying theoretical models in an applied environment

  • Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms

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

Preferred Qualifications

  • Demonstrated expertise in computer vision and machine learning techniques for robotic perception

  • Proficiency with sensor technologies and expertise in machine learning frameworks (e.g. TensorFlow, PyTorch)

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.

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