Software Development Engineer, Ring AI (Edge AI), RBKS AI

With affordable solutions that work on any home Ring is committed to offering smart security thats accessible to everyone. Now Ring AI is seeking experienced software engineers to build the on-device intelligence that powers the next generation of Ring cameras and doorbells. What will you help us create

Youll develop Edge AI software for embedded systems that protect millions of homes worldwide. Your work will bring computer vision and machine learning models onto resource-constrained devices turning raw video into meaningful signals for our customers in real time on-device. Beyond leveraging AI agents and Generative AI tools youll also contribute to AI-powered workflows that are reshaping how embedded software gets built. Backed by Amazons world-class AI resources and infrastructure youll have the support to continuously explore emerging AI technologies enhance your skills and drive innovation in product development.

Key job responsibilities
As a Software Development Engineer on Ring AI (Edge AI) youll be a core member of the team building the on-device perception stack. Youll collaborate with engineers and applied scientists across multiple countries gaining exposure to diverse perspectives and global-scale product development. This is a unique opportunity to grow your career within a multi-national technology leader while working alongside some of the brightest minds in embedded computer vision.

The ideal candidate would be passionate about building intelligent IoT products and eager to push the boundaries of what neural networks can do on constrained embedded targets. You must be responsive flexible and able to succeed within an open collaborative peer environment.

The ideal candidate:

- Is enthusiastic about developing and adopting AI/ML technologies and tools to enhance both product features and software development practices
- Enjoys working side by side with partners colleagues and teams across different countries and time zones
- Is highly effective and thrives in a dynamic environment with multiple changing priorities
- Knows what is important when shipping products to customers and has been through the process from start to finish
- Is comfortable with proactive outward communication and technical leadership and never shies away from a challenge

Work hard. Have fun. Make history.

- 4 years of non-internship professional software development experience
- 3 years of non-internship design or architecture (design patterns reliability and scaling) of new and existing systems experience
- Bachelors degree or above in Computer Science Computer Engineering Electrical Engineering or related fields
- Experience in embedded development in C/C or experience in computer architecture
- Experience building complex software systems especially involving deep learning machine learning and computer vision that have been successfully delivered to customers
- Knowledge of one or more ML Frameworks (e.g. PyTorch TensorFlow) and ML methods including NLP models (BERT GPT-2/3) computer vision-based models (object detection image recognition) and text-based models (Seq2Seq Topic modeling)
- Experience with AI/ML technologies
- Experience with Linux/Unix operating systems
- Experience developing software for embedded Linux or RTOS environments

- 4 years of full software development life cycle including coding standards code reviews source control management build processes testing and operations experience
- Masters degree or above in Computer Science Computer Engineering Electrical Engineering or related fields
- Hands-on experience with FreeRTOS or a comparable RTOS (Zephyr ThreadX NuttX) including task scheduling synchronization primitives and writing ISR-safe code
- Experience designing software under strict latency memory or power budgets
- Familiarity with modern edge model architectures MobileNet YOLO family SSD small transformer variants RNN / GRU / LSTM for temporal tasks
- Experience with object detection multi-object tracking (Kalman IoU Hungarian-style) or motion classification pipelines

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