Senior Computer Vision Research Engineer — Face Analytics & 3D Reconstruction

优缺点分析

角色解读

• Progress to Principal Engineer or Lead Researcher, driving long-term vision for face analytics and XR AI.
• Transition to technical management, overseeing a team of researchers and engineers developing multimedia AI solutions.
• Deepen expertise in generative models and on-device AI, becoming a domain authority in edge-based computer vision.
• Design and optimize deep learning models for face analytics and 3D reconstruction, targeting deployment on smartphone and XR devices.
• Implement efficient architectures (e.g., transformers, diffusion models) to advance state-of-the-art face synthesis and recognition.
• Collaborate with research teams to publish findings at top-tier conferences and integrate algorithms into commercial products.
• Strong theoretical background in computer vision and deep learning, with practical experience using PyTorch or TensorFlow.
• Proficiency in Python and C/C++ for algorithm development and system integration.
• Familiarity with face-related tasks: expression recognition, synthesis, 3D reconstruction, and face editing.

申请策略

• Tailor your cover letter to demonstrate passion for applied computer vision research and how your work aligns with Qualcomm's product lines (Snapdragon, XR).
• Prepare to discuss your research in the context of commercialization—how your algorithms could run efficiently on mobile hardware.
• Emphasize publications in top CV/DL conferences, especially on face analytics, 3D reconstruction, or generative models.
• Showcase experience with PyTorch/TensorFlow and end-to-end model development from idea to deployment.
• Highlight any work on model optimization (pruning, quantization) for edge devices or camera systems.
• Deepen understanding of diffusion models and vision-language models (VLMs) as they are mentioned in preferred qualifications.
• Build experience with face editing and high-resolution synthesis techniques, such as StyleGAN or DiT.

面试指南

• Use the STAR method (Situation, Task, Action, Result) for behavioral questions, emphasizing your role and impact.
• For technical questions, start with the problem statement, then walk through your approach, including trade-offs and why you chose it.
• When discussing research, connect your work to broader trends and potential applications, showing both depth and breadth.
• Explain the architecture of a recent face reconstruction model you worked on and how you optimized it for real-time inference.
• How would you approach 3D face reconstruction from a single 2D image? Discuss challenges and potential solutions.
• Describe a time you had to balance model accuracy with computational efficiency for an edge device.
• Compare transformer-based and CNN-based approaches for face analytics
• when would you choose one over the other?