Deep Learning Compiler Engineer
Develops compiler algorithms and optimization passes to lower and optimize deep learning and high-performance computing workloads for Quadric’s edge-focused neural processing architecture. Requires advanced computer science education, eight or more years of industry experience, and expertise in optimization, graphs, and machine-learning algorithms.
About the job
Responsibilities
- Drive the lowering and optimization of cutting-edge deep neural networks using Quadric’s technology.
- Apply mathematical and algorithmic optimization skills to solve NP-hard problems.
- Collaborate with the software team to develop algorithms that optimize graph-based execution on the Quadric architecture.
Requirements
- Master’s or Ph.D. in Computer Science or a related field.
- Minimum of eight years of industry experience.
- Strong background in numerical and/or algorithmic optimization.
- Understanding of application-appropriate heuristics for NP-hard problems.
- Knowledge of classical and machine-learning algorithms, including computer vision, DSP, and DNNs.
- Strong background in graphs and related algorithms.
Nice-to-haves
- Proficiency in C++11 or later.
- Experience using or developing in TVM or MLIR.
- Knowledge of front-end and back-end compiler techniques.
Expected Outcomes in 12 Months
- Develop a deep understanding of the hardware platform and low-level software to optimize application performance.
- Implement optimization passes for efficient lowering of deep learning and high-performance computing algorithms on the Quadric EPU parallel processor.
Benefits
- Competitive salary and meaningful equity.
- A collaborative, politics-free environment for making an immediate impact.
- Opportunities to build long-term career relationships.
- An environment that supports lasting personal and professional relationships.
Skills
C++, Tvm, Mlir, Compiler Optimization, Numerical Optimization, Algorithmic Optimization, Graph Algorithms, Computer Vision, Dsp, Deep Neural Networks, Machine Learning, Parallel Computing
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