Focus on research and development related to the optimization of ML models on GPU’s or AI accelerators
Use judgment in complex scenarios and apply optimization techniques to a wide variety of technical problems
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

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
Master’s or Ph.D. in a related field and/or 5+ years of experience in a directly related field (a plus)
Computer vision experience (a plus)

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
The opportunity to work on one of the most interesting, impactful problems of the decade

Research Engineer, Optimization

Key skills