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ProductAWS Machine Learning accelerators are at the forefront of AWS innovation. The Inferentia chip delivers best-in-class ML inference performance at the lowest cost in cloud. Trainium delivers the best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by edge software stack, the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, runtime and natively integrates into popular ML frameworks, such as PyTorch and JAX. AWS Neuron and Trainium are used at scale with customers and partners like PyTorch, Anthropic, Poolside, Decart, Epic Games, Snap, AirBnB, Autodesk, Amazon Alexa, and more customers in various other segments.

The TeamThe Amazon Annapurna Labs team is responsible for building innovation in silicon and software for AWS customers.

We are at the forefront of innovation by combining cloud scale with the world's most talented engineers. Our team covers multiple disciplines including silicon engineering, hardware design and verification, software and operations. With such breadth of talent, there's opportunity to learn all of the time. We operate in spaces that are very large, yet our teams remain small and agile. There is no blueprint. We're inventing. We're experimenting. When you couple that with the ability to work on so many different products and services, it's a very unique learning culture.

Learn More About Our HistoryLearn more about our history here.

YouWe are seeking a talented SW Engineering Manager with strong leadership/mentoring skills to join our Deep Learning Compiler Team. As a Manager III you will be leading a team of experienced compiler engineers developing compiler optimization algorithms and deploying, at scale, a new compiler targeting AWS custom hardware. You'll need to be technically capable, credible, and curious in your own right as a trusted AWS Neuron Manager, innovating on behalf of our customers. You'll leverage your technical communications skill as a hands-on partner to AWS ML services teams, involved in pre-silicon design, bringing new products/features to market. As deep learning models become more versatile, using compiler technologies to achieve both high performance and high productivity becomes essential. Join the team to build the software that will boost the entire deep learning community.

You will have deep knowledge of resource management, scheduling, code generation, optimization, and new instruction architectures including CPU, NPU, GPU and novel forms of compute.

Explore The ProductExplore the product here.

About The TeamInclusive Team Culture Here at Annapurna Labs, 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 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.

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. 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.