Deep Learning Inference Edge SDE
- Palo 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 Neo Edge 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.
nclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Our team puts a high value on work-live 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.
This position involves on-call responsibilities, typically for one week every two months. We don't like getting paged in the middle of the night or on the weekend, so we work to ensure that our systems are fault tolerant. When we do get paged, we work together to resolve the root cause so that we don't get paged for the same issue twice.
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.
• Master's Degree in Computer Science or Engineering
• 3+ years of software development experience in an embedded system platform for a shipping product. Hands on experience with multi-threaded software stacks.
• 3+ years C/C++ experience
• 2+ years of software development experience in system security - encryption, access control, auditing, trusted execution, SELinux, container security, or related technologies
• 4+ 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.
• 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
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.
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