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Senior Computer Vision Engineer

Santa Clara, CA
XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.
 
We are seeking a talented Computer Vision / Machine Learning Engineer to join our global team. In this role, you will develop and optimize multi-modal models and computer vision systems, driving performance, efficiency, and real-world deployment. The ideal candidate has hands-on experience with multi-modal model training and optimization, a strong foundation in computer vision, and solid C++ engineering skills.

Key Responsibilities

  • Research and implement multi-modal large models (image-text, image-audio, etc.) training, fine-tuning, and inference optimization strategies, continuously improving model performance, efficiency, and generalization ability.
  • Design and optimize computer vision models and algorithms (e.g., detection, classification, segmentation, feature extraction) to support real-world applications.
  • Collaborate with cross-functional teams (product, engineering, data) to translate research into scalable, reliable, and production-ready solutions.
  • Use C++ to implement and optimize models and systems, including deployment, performance tuning, and integration, ensuring low latency and high throughput.
  • Stay up to date with advances in computer vision and multi-modal AI, and apply new methods to improve model performance and product impact.
  • Contribute to technical discussions, code reviews, and knowledge sharing to improve code quality and engineering best practices.

Minimum Qualifications

  • Master’s or Ph.D. in Computer Science or a related field, with strong expertise in computer vision and machine learning.
  • 1-3 years of experience in multi-modal large model training, fine-tuning, and optimization (e.g., CLIP, Flamingo, BLIP, or self-developed multi-modal models), with a deep understanding of multi-modal fusion mechanisms.
  • Strong foundation in computer vision, including object detection, image classification, feature matching, and image enhancement.
  • Strong C++ development skills, with proficiency in STL, multi-threading, memory management, and performance optimization; experience in production-level implementation and deployment is required.
  • Familiar with deep learning frameworks (e.g., PyTorch, TensorFlow) and computer vision libraries (e.g., OpenCV, OpenMMLab).
  • Strong problem-solving ability, self-driven, and passionate about technological innovation; ability to work independently and in a team.

Preferred Qualifications

  • Experience in edge device algorithm deployment, published papers in top computer vision conferences (CVPR, ICCV, ECCV), or open-source project contributions in related fields.

What do we provide:

  • A fun, supportive and engaging environment.
  • Opportunity to make a significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Opportunity to work on cutting edge technologies with the top talent in the field.
  • Competitive compensation package.
  • Snacks, lunches and fun activities.
 
The base salary range for this full-time position is $174,720 - $295,680 in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
 
We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

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