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 Suite™ 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. Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified. EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws. Benefits Summary for US-Based, Full-Time Employment Positions SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.

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