Job Description
About the Company :
Our client is a company building the world's highest-performance pure digital AI inference chip.
Responsibilities :
- Advance the state of the art in compiler and runtime technology for delivering high-performance acceleration of AI workloads across a variety of neural network architectures.
- Research and design new software and hardware AI solutions, involving simulators, optimizing compilers, code generators, and runtime execution frameworks for deep learning accelerators.
- Evaluate various trade-offs of different parallelization strategies such as performance, power, energy, and memory consumption.
- Enhance AI software tools to support the latest DNNs emerging from the research community and industry.
- Keep up with the fast-paced development happening in the industry and academia to continuously enhance our products.
- Work closely with other software and hardware engineers to develop the next generation of deep learning software.
- Collaborate with architects and hardware engineers to co-design future accelerators.
Preferred Skills & Experience :
- 10+ years of experience developing software for highly parallel architectures.
- Experience with optimizing algorithms for hardware acceleration, machine learning accelerators, spatial architectures, or GPUs.
- Strong problem-solving skills and the ability to resolve complex issues with a high level of ambiguity.
- Understanding of Deep Learning fundamentals.
- Strong development skills in C / C++, Python.
- Excellent soft skills: ability to work efficiently and effectively in a team environment, and influence cross-functional teams without direct managerial authority.
- Degree in Computer Science, Engineering, or a related field; preferably MS or PhD.
- Driven and self-directed.
Perks :
- 20 vacation days
- Strong health and extended health benefits
- Unlimited sick days
- Stock options
Please apply even if you don't meet all the qualifications. We are an inclusive and diverse company welcoming applicants from all backgrounds.