Research Engineer, Optimization
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Requirements
• Proficiency in ML model development and optimization techniques (e.g. numerical optimization, quantization, sparsity, pruning, architecture search and design), particularly on model deployment onto GPU’s or AI accelerators • Strong understanding of deep learning algorithms, software engineering and GPU-based computing • Experience working with neural networks in Tensorflow and/or PyTorch • Proven ability to thrive in fast-paced environment • Ability to communicate complex technical concepts to colleagues and a variety of audience • Introspection, thoughtfulness, and detail-orientation • Proficiency in Python • The following are a plus, but not required: • Master’s or Ph.D. in a related field and/or 5+ years of experience in a directly related field
Responsibilities
• You will focus on research and development related to the optimization of ML models on GPU’s or AI accelerators. You will use your judgment in complex scenarios and apply optimization techniques to a wide variety of technical problems. Specifically, you will: • Research, prototype and evaluate state of the art model optimization techniques and algorithms • Characterize neural network quality and performance based on research, experiment and performance data and profiling • Incorporate optimizations and model development best practices into existing ML development lifecycle and workflow. • Define the technical vision and roadmap for DL model optimizations • Write technical reports indicating qualitative and quantitative results to colleagues and customers • Develop, deploy and optimize deep learning (DL) models on various GPU and AI accelerator chipsets/platforms
Benefits
• Competitive health insurance options • 401K plan management • Free lunch and fully-stocked kitchen in our South Bay office • Additional perks: monthly wellness stipend, office set up allowance, company retreats, and more to come as we scale