AWS Machine Learning accelerators are at the forefront of AWS innovation. Trainium delivers best-in-class ML training performance with the most teraflops (TFLOPS) of compute power for ML in the cloud. This is all enabled by the AWS Neuron Software Development Kit (SDK), which includes an ML compiler, the Neuron Kernel Interface (NKI) compiler, and a runtime that natively integrates into popular ML frameworks such as PyTorch and JAX.
Neuron Kernel Interface (NKI) is a bare-metal language and compiler for directly programming AWS Trainium instances. You can use NKI to develop, optimize, and run new operators directly on hardware while making full use of available compute and memory resources.
Explore NKI:
- https://awsdocs-neuron.readthedocs-hosted.com/en/latest/nki/index.html
AWS Neuron is used at scale by customers such as Epic Games, Snap, Airbnb, Autodesk, Amazon Alexa, and Amazon Rekognition, along with many others across a range of segments.
Amazon's Annapurna Labs is responsible for building innovative silicon and software for AWS customers. We are at the forefront of innovation, 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 is opportunity to learn all 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 makes for a unique learning culture.
Learn more about our history:
- https://www.amazon.science/how-silicon-innovation-became-the-secret-sauce-behind-awss-success
Explore the Product:
- https://aws.amazon.com/machine-learning/neuron/
- https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/nki/index.html
As a Sr. ML Compiler Engineer on the Amazon Neuron team, you are a thought leader supporting the ground-up development and scaling of a compiler to handle the world's largest ML workloads. Architecting and implementing customer-critical features, publishing research, and mentoring a skilled team of engineers are exciting challenges for you. You leverage your technical communications skill as a hands-on partner to Amazon ML partner teams. You want to be involved in pre-silicon design, bringing new products and features to market, and shipping high-quality code.
A background in Machine Learning and AI accelerators is helpful, but not required.
In order to be considered for this role, candidates must be located in or willing to relocate to Seattle or Cupertino.
Key job responsibilities
- Design and build compiler features for the Neuron compilers. Your work may span the frontend, intermediate representations, optimization passes, and code generation for the hardware.
- Own customer-critical features end to end: scope the problem, write the design, ship production code, and provide support to customers.
- Partner with Amazon ML teams (PyTorch, JAX, and internal model teams) as a hands-on technical contact. You translate their workloads into compiler requirements and unblock them on hardware.
- Contribute to pre-silicon design. You give the hardware teams compiler and programmability feedback before a chip is built, and you bring new products and features to customers.
- Raise the technical bar by reviewing code and designs, publishing research where it makes sense, and mentoring engineers.
A day in the life
You start the day reviewing a teammate's code for a new compiler feature, leaving feedback before it merges. Mid-morning, you pair with a model team blocked on a kernel that hasn't reached peak performance on Trainium, and you trace the bottleneck through the compiler's passes. After lunch, you write and test the feature you scoped last week, then run your changes on hardware to confirm correctness. You close the day in a pre-silicon review, giving the hardware team programmability feedback on the next chip. The team moves quickly and you are always in the details.
