Software Engineer II
Indexed description
Blaize is expanding from edge AI hardware into a full platform that combines its hardware systems with Blaize AI Services, the software and services layer that delivers deployable AI solutions for enterprises, governments, and cloud service providers worldwide. By combining differentiated computing architecture with scalable AI Services, we help organizations deploy intelligence where decisions matter most.
To learn more, visit www.blaize.com or follow us on LinkedIn at @blaizeinc.
Description
Blaize is seeking a Software Engineer – II to join the SDK – DNN Performance library team. In this role, you will design, develop, and optimize operators and kernels within the Blaize SDK, enabling high-performance execution on current and next-generation Blaize hardware. You will work closely with compiler, hardware, and ML teams to deliver efficient and scalable solutions
Responsibilities
- Design, implement, and maintain operators and kernels within the Blaize SDK and perlib.
- Optimize operator performance and improve execution efficiency across workloads.
- Enable and support new features and performance improvements for next-generation Blaize chips.
- Collaborate with cross-functional teams including hardware, compiler, and ML engineers.
- Analyze performance bottlenecks and implement optimizations at the graph and kernel levels.
- Bachelor’s or master’s degree in computer science or a related field.
- 5–8 years of hands-on software engineering experience, preferably in performance-critical systems.
- Strong proficiency in C and C++, including extensive use of STL libraries.
- Solid experience with ONNX and/or PyTorch operators, including graph and node-level optimization.
- Experience writing parallel kernels for GPUs or similar accelerator architectures.
- Understanding performance optimization techniques for compute-intensive workloads.
- Basic knowledge of machine learning networks and large language models (LLMs) is a plus.
- C, C++
- Data Structures and Algorithms
- STL Libraries
- ONNX / PyTorch Operators
- Graph and Node Optimization
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