Applied Machine Learning Engineer

Palo Alto, CA

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SambaNova Systems

SambaNova delivers the first full-stack generative AI platform, from chip to model, designed for enterprise and government entities and powered by a dataflow architecture. SambaNova delivers the accuracy, transparency, and ownership of models...

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Applied Machine Learning Engineers at SambaNova Systems are our experts at developing and deploying machine learning models. They stay up-to-date with the latest academic research in deep learning and run experiments to investigate statistical and systems tradeoffs. They are responsible for driving innovation in models and algorithms that run on the SambaNova platform and provide an ML-focused perspective to other teams. Applied machine Learning (ML) engineers therefore often operate at the intersection of algorithms and software/hardware systems at SambaNova.

SambaNova leads the trend of large-scale deep learning solutions for industrial domain-specific AI workflows. The machine learning team in SambaNova innovates from strategic product pathfinding to large scale production. Leveraging the special characteristics of SambaNova’s architecture that are not available in existing hardware, we develop unique capabilities in areas such as large language models, high resolution computer vision, ultra-fast time-series and large scale graph neural networks. 

Our efforts focus on delivering high-value workflows for vertical industrial customers in their AI transformation. These industries include but are not limited to financial services, energy, healthcare & life sciences and manufacturing. We are excited to have talents on board, pushing towards democratizing the high-value AI capabilities in real-world use cases. 

Specific Responsibilities:

  • Building state-of-the-art deep learning models on SambaNova’s SW/HW stack
  • Implementing deep learning applications and optimizing the statistical performance and system efficiency
  • Deploy the deep learning application to low-code solution platforms for vertical industries
  • Software / model & hardware co-design for fast training and inference
  • Applied research to solve key data, model and compute challenges in democratizing large-scale AI workflows to real-world use cases for customer success

Skills and Qualifications:

  • Experience with one or more deep learning frameworks like TensorFlow, PyTorch, Caffe2, or Theano
  • Deep theoretical or empirical understanding of modern deep learning models
  • Strong mathematical fundamentals and algorithms skills or experience in decomposing models to core operations
  • Experience building and/or deploying machine learning models
  • Strong programming, test design and debugging skills in Python and/or C++
  • Interest in high performance machine learning systems and performance optimization

AI is here. With SambaNova, customers are deploying the power of AI and deep learning in weeks rather than years to meet the demands of the AI-enabled world. SambaNova’s flagship offering, Dataflow-as-a-ServiceTM, is a complete solution purpose-built for AI and deep learning that overcomes the limitations of legacy technology to power the large and complex models that enable customers to discover new opportunities, unlock new revenue and boost operational efficiency. Headquartered in Palo Alto, California, SambaNova Systems was founded in 2017 by industry luminaries, and hardware and software design experts from Sun/Oracle and Stanford University. Investors include SoftBank Vision Fund 2, funds and accounts managed by BlackRock, Intel Capital, GV, Walden International, Temasek, GIC, Redline Capital, Atlantic Bridge Ventures, Celesta, and several others. For more information, please visit us at sambanova.ai or contact us at info@sambanova.ai. Follow SambaNova Systems on LinkedIn.

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Computer Vision Dataflow Deep Learning Industrial Machine Learning ML models Oracle Python PyTorch Research TensorFlow Theano

Region: North America
Country: United States
Job stats:  10  1  0

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