- Location
- San Diego, CA,US, US
- Type
- Full-time
- Department
- Engineering
- Seniority
- Director
- Education
- PhD
- Source
- Eightfold
Description
Architect high-performance, power-efficient ML hardware accelerator designs, especially for computer vision workloads (e.g., image classification, object detection, video analytics). Analyze and optimize hardware for performance, power, and area (PPA) tradeoffs. Define hardware micro-architecture for efficient implementation of ML algorithms. Collaborate closely with global hardware, software, and systems teams to deliver best-in-class ML solutions. Work with cross-functional teams (HW design, verification, SW, systems, marketing, product planning) to align architecture with product goals. Apply knowledge of CPUs, GPUs, DSPs, memory, and bandwidth analysis to ML hardware design. Architecture/micro-architecture of ML or multimedia cores. Experience in hardware development for ML, multimedia, or accelerator technologies (vision, imaging, video, display, audio, etc.). Experience in system or chipset development for SoC products, preferably in the mobile market. Familiarity with OS principles and HW/SW interaction. Experience with SoC bus, interconnect, and memory technologies. Experience with ML frameworks and accelerator standards (e.g., TensorFlow Lite, ONNX). Deep domain knowledge in ML for vision (image/video processing, neural network architectures for vision). Experience collaborating with global teams across multiple time zones and cultures. Experience working with synthesis and physical design teams for HW IP development and PPA improvement. Excellent communication, documentation, and presentation skills. Demonstrated technical and people leadership. Bachelor's degree in Computer or Electrical Engineering, Computer Science, or related field and 8+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer or Electrical Engineering, Computer Science, or related field and 7+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience. OR PhD in Computer or Electrical Engineering, Computer Science, or related field and 6+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
