ashby
Principal Software Engineer, Kernels
D Matrix
Santa Clara, USA
Full-time
Senior · 12+ years experience
Hybrid
Discovered 1 weeks ago
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Location:
- Hybrid, working onsite at our Santa Clara, CA, headquarters 3-5 days per week.
- The Role: Principal Software Engineer, Kernels
What you will do:
- The role requires you to be part of the team that helps productize the SW stack for our AI compute engine. As part of the software team, you will be responsible for the development, enhancement, and maintenance of software kernels for next-generation AI hardware. You possess experience building software kernels for HW architectures. You possess a very strong understanding of various hardware architectures and how to map algorithms to the architecture. You understand how to map computational graphs generated by AI frameworks to the underlying architecture. You have had past experience working across all aspects of the full-stack toolchain and understand the nuances of what it takes to optimize and trade off various aspects of hardware-software co-design. You can build and scale software deliverables in a tight development window. You will work with a team of compiler experts to build out the compiler infrastructure, working closely with other software (ML, systems) and hardware (mixed signal, DSP, CPU) experts in the company.
- What you will bring:
- Minimum:
- MS in computer engineering, math, physics, or a related degree with 12+ years of industry experience or a PhD in computer engineering, math, physics, or a related degree with 7+ years of industry experience.
- Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals.
- Proficient in C/C++ and Python development in Linux environments and using standard development tools.
- Experience implementing algorithms in high-level languages such as C/C++ and Python.
- Experience implementing algorithms for specialized hardware such as FPGAs, DSPs, GPUs, and AI accelerators using libraries such as CUDA, etc.
- Experience in implementing operators commonly used in ML workloads—GEMMs, Convolutions, BLAS, SIMD operators for operations like softmax, layer normalization, pooling, etc.
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