Hybrid Senior Computer Vision Engineer - Deployment bei XPENG
XPENG · Santa Clara, Vereinigte Staaten Von Amerika · Hybrid
- Professional
- Optionales Büro in Santa Clara
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 passionate and skilled Senior Computer Vision Engineer to lead the development and deployment of high-performance, large-scale AI models. You will focus on optimizing model inference, implementing compression techniques (quantization, pruning, distillation), and ensuring efficient on-device deployment across GPU and custom AI accelerator platforms. Your work will directly enable the next generation of intelligent systems in autonomous driving and beyond.
Key Responsibilities:
- Optimize large-scale multimodal models for low-latency inference and efficient memory usage across diverse hardware platforms.
- Apply state-of-the-art model compression techniques, including quantization (e.g., INT8/FP16), pruning, and knowledge distillation.
- Develop and integrate custom inference kernels targeting GPU or custom AI accelerators.
- Build profiling tools and performance models to analyze bottlenecks and guide optimization strategies.
- Contribute to real-world deployment efforts in autonomous driving systems, including on-vehicle testing and iteration.
- Track the latest research in efficient ML inference and integrate relevant techniques into production pipelines.
Minimum Requirements:
- Master’s or Ph.D. in Computer Science, Electrical Engineering, or related field. Open to recent graduates.
- Strong coding skills in C++ and Python with a focus on performance and scalability.
- Proficient in deploying deep learning models using TensorRT, ONNX Runtime, or TVM.
- Familiarity with CUDA programming and parallel computing principles.
- Solid understanding of model inference workflows and system-level performance tuning.
- Experience in quantization-aware training or post-training quantization.
- Effective communicator and collaborative team player.
Preferred Qualifications:
- Hands-on experience with deploying vision-language or large multimodal models.
- Familiarity with low-precision inference (INT8/FP16), kernel fusion, and operator-level optimization.
- Experience in autonomous driving, robotics, or edge AI applications.
- Track record of open-source contributions or publications in ML/AI conferences (e.g., NeurIPS, ICML, CVPR).
- Background in system profiling, latency modeling, or compiler-level optimization.
What do we provide:
- A fun, supportive and engaging environment
- Infrastructures and computational resources to support your work.
- Opportunity to work on cutting edge technologies with the top talents in the field.
- Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving
- Competitive compensation package
- Snacks, lunches, dinners, 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.
