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Amazon Sr. Embedded Software Development Engineer, Ring AI in Sunnyvale, California

Description

The Ring AI team is looking for a Sr. Embedded Software Development Engineer to support the development of Computer Vision (CV) / Machine Learning (ML) Edge SW and Research Infrastructure. In this role, you will work with our passionate, talented, and inventive engineers and scientists, to develop industry-leading CV and ML systems.

Key job responsibilities

You will be leading a cross-functional development team, responsible for implementing and improving computer vision and machine learning algorithms that deliver high-performance detection and classification at exceptionally low power consumption. As a senior member of the team, you will oversee the deployment of software to millions of existing Ring devices, and improve customer experience with cutting-edge SOCs and software techniques in new products.

A day in the life

You will work closely with applied science teams working on computer vision algorithms and help implement on device to deliver exciting features for customers. You will be responsible for leading and delivering on large efforts with other engineers and also help establish best practices and improve operational excellence within the team.

We are open to hiring candidates to work out of one of the following locations:

Sunnyvale, CA, USA

Basic Qualifications

  • 5+ years of programming with at least one software programming language experience

  • 5+ years of non-internship professional software development experience

  • 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience

  • Bachelor's degree in computer science or equivalent

  • Experience as a mentor, tech lead or leading an engineering team

  • Knowledge of professional software engineering & best practices for full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence

  • Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems

  • Experience in embedded development in C/C+- Experience with the deployment and/or development of Machine Learning and Deep Learning models on Edge

  • Excellent communication and interpersonal skills

  • Excellent data analysis and troubleshooting skills

Preferred Qualifications

  • Master's degree in computer science or equivalent

  • Knowledge of ARM CPUs

  • Experience implementing computer vision machine learning algorithms on edge

  • Knowledge of Unix/Linux environments and embedded Linux

  • Experience with software testing (unit, integration, and system testing)

  • Experience with Python and tool creation

  • Experience working with distributed systems or applications

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 $151,300/year in our lowest geographic market up to $261,500/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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