Annapurna Labs, is an organization within AWS, that is responsible for building custom silicon and software for AWS customers. With development centers in the U.S. and Israel, Annapurna is at the forefront of innovation by combining cloud scale with the world’s most talented engineers. The Annapurna team covers multiple disciplines including silicon engineering, hardware design and verification, software, and operations.

As part of Annapurna Labs Silicon Validation team, you’ll have the opportunity to validate the next generation of cloud computing machine learning accelerators. You’ll experience what it’s like to work in a fast-paced, innovative, and start-up like environment filled with some of the brightest minds in the industry. The work we do is not only cutting-edge and internet-scale but also deeply important to our customers. The team's validation is used to test every component of our hardware and software to come together into products that our customers use for Machine Learning acceleration.

Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world. The scope of this role includes validation of the vertical stack of Silicon, PCB, High Speed components e.g., HBM, PCIe and Chip to Chip, inter-systems and system to system. Diving deep into new technology hardware components and scaling technologies that power our Machine Learning boards and servers at scale.

Key job responsibilities
As a Senior Validation engineer, you are responsible for validating the chip and system architecture for the next generation Machine Learning Acceleration (MLA) product family. You work closely with design, software & architecture teams and develop in-depth chip and system validation plans, execute on the validation plans, debug and take the MLA product to successful production ramp. This includes validating the design to achieve the best performance, identify bottlenecks and work with design/architecture teams to resolve them. Our work does not stop at production ramp, we continue to support our servers in the datacenter through their lifecycle.

A day in the life
* Architect system validation strategies
* Develop system validation methodologies and infrastructures for validating machine learning HW and neural networks
* Evaluate and improve performance of Machine Learning systems
* Work with other functional teams to ensure delivery of high quality systems to AWS data centers
* Own validation schedules and reporting of progress metrics
* Provide technical mentoring for junior team members

Post-Silicon Systems Validation Engineer, Annapurna Labs