Computer Vision Engineer V

C + +, C +, Computer Vision, Deep Learning, Machine Learning, Python, Pytorch, Tensorflow, software development, H/W Acceleration, Semi-conductor, Imaging, CNN, ML/CV
Contract W2, 12 Months
Depends on Experience
Work from home available

Job Description

Computer Vision Engineer V

100% Remote 

12 Months 

Job Summary:

We are looking for an Engineer to implement efficient h/w acceleration of Machine Learning algorithms for Computer Vision in embedded domain, with an emphasis on performance and power. We are looking for someone with strong software development skills, familiarity with ML algorithms like CNN’s and hands-on experience in s/w-h/w codesign, especially in the context of ML.
Work include:
1. Collaborate with computer architects, software, ML and silicon engineers, to map and optimize ML workloads on various backend targets including CPU’s, DSP’s and Deep Learning Accelerators.
2. Run analysis/profiling , identify performance and power bottlenecks on the actual h/w, virtual platforms, simulators or emulators and provide feedback for optimizations across the entire stack.
3. Work with Deep Learning compilers; identify the correct knobs for best efficiency and influence new feature additions.
4. Develop optimized kernels and automate compilation of various ML algorithms to backends like DSP’s with custom ISA;
5. Ensure high quality by creating tests and automation infrastructure.
6. Partner with productization teams and driver/firmware teams to integrate ML acceleration into shipping software and create any new tools as necessary.
• BS in EE/Computer with 5+ industry experience. MS or PhD with industry experience is preferable.
Strong coding skills in C/C++ or Python. Must have experience with semiconductor.
• Experience with h/w acceleration on GPU’s/CPU’s/DSP’s/custom h/w. 
• Familiarity of ML algorithms like CNN’s and frameworks like Tensorflow/Pytorch.
• Familiarity with profiling and debug tool; Tools in context of ML is a plus.
• Familiarity with Deep learning compilers like tensor-rt, XLA is a plus.
• Understanding of ML algorithm optimizations for low power like quantization, pruning etc. is a plus.
• Comfortable with reading others code, tracing them, and code refactoring

-Interview Process-

1 phone screening

2 coding evaluation 

Dice Id : 10119021
Position Id : 7529141
Originally Posted : 1 week ago
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