SW Engineer, Machine Learning Appliance

• 5+ years experience designing and building large scale backend systems
• Strong fundamental understanding of OO design, algorithms, and data structures
• Expertise in Python, Flask, Django, Docker, RabbitMQ, and OAM suites such OpenStack, Mesos, and/or Kubernetes
• Highly desirable, but not strictly required, skills include experience with DevOps, AWS, KVM, SELinux/MAC, NAS, Paxos, highly available distributed systems, performant scale-ready Python, resource-based dynamic scheduling, and system languages such as C, Go or Rust.

Nervana benefits

• Medical plans: Health, vision, dental
• Life insurance and 401k plans
• Flexible work schedule
• Great stock options
• Each engineer gets a top of the line Macbook Pro and access to the fastest GPU clusters
• Annual company retreat
• Small teams
• Fun office spaces with many toys!
• Our Palo Alto office is a part of Playground Global & our San Diego office is 10 minutes from beautiful beaches
• Free healthy snacks and beverages

About Nervana

Nervana provides “AI on demand”. Businesses use our Nervana Cloud platform to create and deploy solutions that include natural language processing, image recognition, computer vision and other types of artificial intelligence. We specialize in “deep learning”, which is the technology that powers Apple’s Siri, Facebook face recognition, and Google’s self-driving cars and AlphaGo. The space is white hot right now! A bit more about Nervana:

• Closed a $20M+ Series A round last year from DFJ, Allen & Company, and others.
• Offices in both San Diego and in the ultra-cool Playground space in Palo Alto.
• Current customers in industries such as agribusiness, financial services, health services, automotive, and the intelligence community.

Find out more about Nervana at www.nervanasys.com.

What you will be working on

• Design, create and deploy new features for our state-of-the-art Machine Learning-as-a-Service platform. These features span both front- and back-end functionality, such as the containerization and intelligent scheduling of highly interesting deep learning workloads.
• Transparently and securely adapt our existing cloud-centric platform onto a stand-alone, hardened appliance using carefully designed abstraction layers.