Annapurna Labs designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago-even yesterday. 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.
In Annapurna Labs, we are at the forefront of hardware/software co-design not just in Amazon Web Services (AWS) but across the industry.
The Machine Learning Acceleration Fleet Operations Team is looking for candidates interested in diving deep into our fleet of ML servers deployed around the world. We are seeking an engineer who is comfortable debugging emergent problems in GPU and server hardware, writing scripts in languages such as Python or Bash, running large scale experiments on a fleet of complex hardware, developing data infrastructure and analyzing trends, and developing automation software to scale operations.
Our team has end-to-end ownership of some of the most advanced server hardware in the world. We drive technical debug efforts and write truly massive scale autonomous software to monitor, optimize, and remediate machine learning hardware. Come join us!
Key job responsibilities:
• Member of a team responsible for system remediation, operational excellence, and customer experience on bleeding edge ML products • Utilize data to root cause hardware failures and identify live trends on the most complex systems in AWS • Implement and improve system level testing across the product lifecycle • Develop software which can be maintained, improved upon, documented, tested, and reused • Dive deep on issues at the intersection of hardware and software
A day in the life:
As a Platform Development Engineer, you are the dedicated owner of an ML server platform in our fleet. Your mission is to maximize its health, sellability, and customer experience.
You start each day with eyes on the fleet - reviewing dashboards to identify trends and triaging emergent issues, then partnering with hardware and software engineering teams to debug, investigate, and translate findings into permanent fixes. You own the end-to-end testing story and manage tradeoffs between coverage and velocity. You direct new automations, tooling, and data infrastructure to scale your operations. You manage software deployments, debug issues with them, and run status meetings to align all platform stakeholders on how the product is performing.
About the team:
The MLA Fleet Operations team was formed to maintain an exceptionally high quality bar for our fleet of advanced machine learning accelerators and server products. We perfect the customer experience by developing scalable software for rapid incident response times and data visualization as well as diving deep into hardware issues as they arise.