System Software Engineer

• Bachelor’s, Master’s or Ph.D. in Computer Science or Computer Engineering
• 5+ years developing commercial quality system software, e.g. compilers, drivers, debuggers, profilers
• Strong programming skills in C/C++ is required, experience with Python is preferred
• Experience with LLVM, GCC, GPU, CUDA, OpenCL, HPC, MPI, distributed systems is preferred
• Skills or experience in areas such as machine learning and computer vision is preferred

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

• Analyses and transformations for optimizing code for Nervana’s custom hardware backend
• Code generation and related transformations for Nervana’s instruction set architecture
• Intermediate Representations and APIs to allow other components to interoperate with the Nervana compiler
• Collaborating with deep learning researchers to streamline our technology stack
• Collaborating with driver developers and hardware architects to influence low level features