Software Engineer, ML Inference Performance
SambaNova Systems
Software Engineer, ML Inference Performance
The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale.
SambaNova Suiteâ„¢ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets.
About The Role
The Principal Compiler Engineer - ML Systems position will be responsible for working with the different layers of the compiler stack and coordinating with other development teams here at SambaNova. It is a critical role responsible for driving innovation in compiler infrastructure and optimization algorithms that enable state-of-the-art ML model performance on the SambaNova platform. This can involve anything from digging through PyTorch and machine learning models to determining how to map operations on to our underlying hardware.
Responsibilities
- Lead compiler engineering through ensuring standard methodologies, enterprise product insertion and process evolution.
- Work with peers, domain experts, developers, customers, and work across the enterprise seeking optimal solutions.
- Develop, integrate, and implement products.
- Provide support for proposals in key areas aligned with core team competencies.
Basic Qualifications
- Bachelor's or Master's Degree in Computer Science, Computer Engineering, or equivalent with 5-10 years of industry experience.
Additional Qualifications
- Deep theoretical understanding of compiler fundamentals.
- Experience building and deploying software products.
- Experience with one or more deep learning frameworks (i.e. TensorFlow, PyTorch) is a plus.
- Experience with common compiler development practices and methodologies.
- Excitement about high-performance systems engineering and performance debugging.
- An appreciation for process and developing cross-disciplinary collaboration.
Preferred Qualifications
- Experience with MLIR.
- Familiarity with machine learning models and frameworks.
- Familiarity with accelerated computing.
- Exposure to dataflow architectures.