Senior Computer Vision Algorithm Engineer – Hardware Co‑Optimization (Edge AI/SoC)

优缺点分析

角色解读

• Become a technical leader in algorithm-hardware co-design, shaping next-generation AI accelerators.
• Transition to a system architect role defining CV/AI product roadmaps.
• Advance to senior technical positions (Principal/Staff Engineer) or move into management leading cross-functional teams.
• Design and optimize computer vision algorithms, focusing on segmentation and real-time performance for edge devices.
• Implement state-of-the-art efficient deep learning architectures such as Transformers, Mamba, and FlashAttention.
• Collaborate with hardware and SoC teams to co-design accelerators and pipelines for CV/ML workloads.
• Profile and optimize model performance across memory, compute, and bandwidth constraints
• contribute to system-level architecture decisions.
• Strong background in computer vision, especially segmentation (classical and deep learning).
• Deep understanding of SoC architecture (compute accelerators, memory hierarchy, hardware/software interfaces).
• Hands-on experience with modern efficient architectures: Transformers, Mamba, FlashAttention, lightweight models.
• Proficiency in Python and C/C++

申请策略

• Tailor your resume to demonstrate cross-disciplinary impact, not just pure algorithm work.
• In your cover letter, express enthusiasm for the hardware-software interface and how you can contribute to both sides.
• Emphasize hands-on projects involving algorithm-hardware co-design, edge deployment, or performance optimization.
• Highlight experience with segmentation algorithms and efficient architectures (Transformers, Mamba, FlashAttention).
• Showcase proficiency in C++ and Python along with frameworks like PyTorch and TensorFlow.
• Include any work with hardware modeling, FPGA prototyping, or SoC-level profiling.
• Deepen knowledge of SoC architecture, memory hierarchies, and compute accelerators.
• Practice with quantization, pruning, and other model compression techniques.

面试指南

• For optimization questions: start by identifying the constraints (memory, compute, latency), then discuss techniques like pruning, quantization, knowledge distillation, and hardware-aware architecture search.
• For architecture comparison: compare key features (attention vs. recurrence, efficiency, scalability) and provide concrete examples from your experience.
• For collaboration questions: highlight your ability to communicate across domains, share clear metrics, and iterate on design trade-offs.
• Can you describe how you would optimize a segmentation model for real-time performance on an edge device with limited memory?
• Explain the differences between Transformer, Mamba, and CNN architectures for vision tasks. When would you choose one over another?
• How does FlashAttention work, and what are its benefits in terms of memory and compute?
• Describe a time you collaborated with hardware engineers to co-design an accelerator for a specific algorithm. What challenges did you face?
• How would you profile and improve the runtime performance of a deep learning model on a custom SoC?