Principal Software Engineer (Kernels)

• 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 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 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

Benefits

• Equity
• Health care
• Flexible time-off
• Paid paternity leave
• 401k retirement plan
• Remote & hybrid working model
• Work with world-leading engineers and researchers
• On-site fitness center
• Free food and snacks
• Peer coaching & development
• Happy hours
• Team-building activities

• 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
• Self-motivated team player with a strong sense of ownership and leadership
• Experience implementing algorithms in high-level languages such as C/C++ and Python
• Proficient in C/C++ and Python development in Linux environments and using standard development tools
• MS in computer engineering, math, physics, or a related degree with 10+ years of industry experience or a PhD in computer engineering, math, physics, or a related degree with 5+ years of industry experience
• Experience in implementing operators commonly used in ML workloads—GEMMs, Convolutions, BLAS, SIMD operators for operations like softmax, layer normalization, pooling, etc
• Experience with development for embedded SIMD vector processors such as Tensilica
• Experience implementing algorithms for specialized hardware such as FPGAs, DSPs, GPUs, and AI accelerators using libraries such as CUDA, etc
• Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals
• Prior startup, small team, or incubation experience
• Experience with ML frameworks such as TensorFlow and/orPyTorch
• Experience working with ML compilers and algorithms, such as MLIR, LLVM, TVM, Glow, etc
• Work experience at a cloud provider or AI compute/subsystem company
• Experience with a deep learning framework (such as PyTorch or TensorFlow) and ML models for CV, NLP, or recommendation