About the Company - We are seeking a highly skilled and experienced Computer Software Architect to lead the design and development of cutting-edge compiler architectures for NPUs (Neural Processing Units), as well as end-to-end pipelines for training, fine-tuning, of a range of state space models, as well as a distillation pipeline for Large Language Models (LLMs). The ideal candidate will have 3-5 years of experience in a similar role, be well-versed in embedded systems or edge-based AI, and possess a strong professional background in software engineering.
About the Role - Key Responsibilities
Compiler Architecture & Design - Using open-source compliers, you will define, architect, and optimize compilers for NPUs, including code generation and optimization techniques for AI workloads. Work closely with hardware and R&D teams to ensure seamless integration of compiler features and hardware capabilities.
Embedded & Edge AI Systems - Adapt and optimize AI models for deployment on embedded systems and edge devices, focusing on performance, memory footprint, and power consumption. Collaborate with product teams to address real-world constraints and ensure robust, efficient performance in edge environments.
Cross-functional Collaboration - Work with software engineering, R&D, and hardware design teams to define project requirements, architectural decisions, and timelines. Provide technical leadership and mentorship to junior engineers and developers.
Quality & Performance Optimization - Establish best practices for code quality, testing strategies, and debugging of complex compiler pipelines. Continuously analyze and improve system throughput, reliability, and efficiency.
Qualifications
Education: Bachelor's or Master's degree in Computer Science, Computer Engineering, or a related field. A Ph.D. is a plus.
Experience: 3-5 years of hands-on experience in compiler architecture, AI model training pipelines, and/or related embedded systems development or equivalent degree.
Technical Expertise: Strong background in compiler internals (e.g., TVM, XLA, or similar) and code optimization strategies. Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow) and related libraries. Understanding of quantization, pruning, and distillation techniques for AI model optimization.
Embedded Systems & Edge AI: Demonstrated experience with low-level programming and embedded or edge-based deployments of AI models.
Software Engineering Proficiency: Strong skills in at least one systems language (C, C++, Rust) and one scripting language (Python, Bash). Version control (Git) and continuous integration (CI) knowledge is a plus.
Soft Skills: Excellent communication, leadership, and problem-solving abilities. Comfortable working in cross-functional, collaborative environments.
Preferred Qualifications
Proven track record of leading complex, multidisciplinary projects from concept to completion.
Experience with hardware abstraction layers or hardware accelerators beyond NPUs (e.g., GPUs, FPGAs).
Prior work on AI model optimization and deployment in resource-constrained environments.
Contributions to open-source compiler or AI projects.