d-Matrix
AI
SoftwareEngineer,Staff-SIMDKernels
Neural analysis suggests this role is
optimal for Staff candidates.
“Software Engineer, Staff - SIMD Kernels at d-Matrix. Skills: SIMD Kernels development, AI compute engine software stack productization, ML operator implementation for next-generation AI hardware, Hardware-software co-design. Productize the software stack for AI compute engine. Develop, enhance, and maintain software kernels for ML operators”
Industry & Context.
Tackling challenges
What They're Looking For.
Must Have
MS or PhD in computer engineering, math, physics, or a related degree with 5+ years of industry experience, grasp of computer architecture, data structures, system software, and machine learning fundamentals, Proficient in C/C++ and Python development in Linux environment and using standard development tools, Experience in implementing algorithms in C/C++ and Python, Experience in implementing algorithms for specialized hardware such as FPGAs, DSPs, GPUs, AI accelerators using libraries such as CUDA, etc., Experience in implementing operators commonly used in ML workloads—GEMMs, Convolutions, softmax, layer normalization, pooling, etc., Self-motivated team player with a sense of ownership and leadership
Nice to Have
Prior startup, small team, or incubation experience, Experience with ML frameworks such as TensorFlow and/or PyTorch, Experience working with ML compilers and algorithms, such as MLIR, LLVM, TVM, Glow, etc., Experience with a deep learning framework (such as PyTorch or TensorFlow) and ML models for CV, NLP, or recommendation, Experiences in development for embedded SIMD vector processors such as Tensilica, Work experience at a cloud provider or AI compute/subsystem company
What You'll Do.
Productize the software stack for AI compute engine
and maintain software kernels for ML operators
Build solutions that make SDK intuitive for developers to use
Deliver and scale high-quality software quickly
How You'll Work.
Team & Collaboration
Work across the full toolchain; Collaboration with team members; Direct communication
Communication Scope
Direct communication
Full Job Description
At d-Matrix, we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration. We value humility and believe in direct communication. Our team is inclusive, and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together, we can help shape the endless possibilities of AI. Location: Santa Clara, CA headquarter or any of our regional offices. Remote possible. The Role: Software Engineer, Staff - SIMD Kernels What you will do: As a member of the SIMD Kernels team, you will help productize the software stack for our AI compute engine. You will develop, enhance, and maintain software kernels for ML operators—such as softmax, layer norm, and activation functions—for next‑generation AI hardware. You will also build solutions that make our SDK intuitive for developers to use and analyze performance. You have experience building software kernels for modern hardware architectures and understand how to map algorithms and AI‑framework computational graphs onto those architectures. You’re comfortable working across the full toolchain and navigating hardware–software co‑design trade‑offs. You can deliver and scale high‑quality software quickly in a fast‑moving development environment. What you will bring: Minimum: - MS or PhD in computer engineering, math, physics, or a related degree with 5+ 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 environment and using standard development tools. - Experience in implementing algorithms in C/C++ and Python. - Experience in implementing algorithms for specialized hardware such
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