Deep Learning Inference Edge SDE - Windows

2 months agoPalo Alto, CA


At AWS AI, we want to make it easy for our customers to deploy machine learning models on any endpoint in the cloud or at the edge. Just as SageMaker provides a complete set of services to simplify the task of building and training a model, Neo provides an inference engine that is designed to run any machine learning model on any hardware. Running machine learning inference on edge devices reduces latency, conserves bandwidth, improves privacy and enables smarter applications, and is a rapidly growing area as smart devices proliferate consumer and industrial applications.

Neo optimizes machine learning models to perform at up to twice the speed of the original framework with no loss in accuracy. Upload a pre-trained model built with MXNet, TensorFlow, PyTorch, or XGBoost to your S3 bucket, choose your target hardware platform from Intel, NVIDIA, or ARM, and with a single API call, SageMaker Neo optimizes the model, converts it into an executable module, and returns it to your S3 bucket. The free open source Neo runtime uses less than 100th of the space of the framework to run the model on the target hardware.

The SageMaker Edge Manager team is building new technology to deliver the most efficient inference on edge devices, coupled with ease of managing these devices. ( We are hiring well-rounded applied scientists and software developers with backgrounds in machine learning, embedded systems, compilers, and AI accelerators. If you have worked on running high performance computations on embedded devices, done performance tuning, developed software stacks on devices with limited compute and memory resources, you will enjoy working on the breadth of ML applications that we optimize.

You will help develop the runtime for machine learning, work with machine learning model compilers, and enable device and model management. You will engage with silicon vendors and device makers on enriching their on-device and device management service needs. The role offers an extremely broad set of opportunities to work as a full stack SDE with exposure to multiple AI applications, ML frameworks, ML models, compilers, embedded software, service software, and various AI hardware including ARM, Intel, AWS Inferentia, and NVidia.

Join the Amazon SageMaker Neo Edge team to help AWS customers deploy machine learning models on edge devices at scale in production. Work on an open source industry-standard compiler and runtime for machine learning that is already deployed on over 20 million devices.

Inclusive Team Culture
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Work/Life Balance
Our team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.


• 2+ years of non-internship professional software development experience
• Programming experience with at least one modern language such as Java, C++, or C# including object-oriented design
• 1+ years of experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems.
• 2+ years embedded system development using C or C++
• 1+ years of Windows development experience
• Familiarity with Windows CI/CD best practices


• 2+ years technical leadership role for ground up design and delivery of key on-device software modules, including writing documentation, designing APIs, doing architecture reviews, and leading more junior engineers.
• 1+ years developing applications for Windows OS targets
• Familiarity with a Machine Learning framework such as TensorFlow, PyTorch, or MxNet.
• Experience with running inference runtimes on embedded platforms
• Experience making optimization trade-offs for Machine Learning operations on various processors

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