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Peter Xiong
13800000000
zhangwei@example.com
Shanghai
28
Male
Machine Learning Engineer
Employed
Shanghai
25k - 35kPersonal Summary
- With [X] years of R & D experience in the field of machine learning, proficient in technologies such as deep learning, recommendation systems, and computer vision.
- Possess rich project practical experience, led and participated in multiple important projects, and achieved remarkable results, such as improving model accuracy and shortening training time.
- Good team cooperation and communication skills, able to lead the team to overcome technical problems and promote project implementation.
- Continuously pay attention to cutting - edge industry technologies, keep learning and innovating, and provide strong support for the company's technological development.
Education Experience
Shanghai Jiao Tong University
985211Double First - Class
Computer Science and Technology
Master
2016 . 092019 . 06
- Systematically studied professional courses such as machine learning, deep learning, data structure and algorithms, with excellent grades, GPA 3.8/4.0.
- Participated in many scientific research projects, such as the research on image recognition based on deep learning, which exercised scientific research ability and team cooperation ability.
Work Experience
ByteDance
Internet GiantTechnological Innovation
Artificial Intelligence Laboratory
Machine Learning Engineer
Algorithm R & DModel OptimizationTeam Management
2019 . 072022 . 12
Shanghai
- Responsible for the R & D and optimization of machine learning algorithms for the company's core products, such as recommendation systems and user portrait construction.
- Led the construction of a user behavior prediction model based on deep learning, which increased the prediction accuracy by 20%, effectively improving user retention rate and activity.
- Led the team to carry out technical research and solve performance bottleneck problems in large - scale data processing and model training, reducing the model training time from 24 hours to 8 hours.
- Closely cooperated with product and business teams, deeply understood business needs, provided accurate technical solutions, and promoted the implementation of multiple projects with good results.
NIO
New Energy VehicleIntelligent Driving
Autonomous Driving R & D Department
Senior Machine Learning Engineer
Autonomous DrivingMulti - Sensor FusionModel Deployment
2023 . 01Present
Shanghai
- Responsible for the development of the machine learning module of the company's autonomous driving project, including environmental perception and behavior prediction.
- Designed and implemented a multi - sensor fusion object detection algorithm, with a detection accuracy of over 95% in complex traffic scenarios.
- Optimized the autonomous driving decision - making model. Through reinforcement learning methods, the rationality and safety of vehicle decisions were significantly improved, and the accident rate was reduced by 30% in actual road tests.
- Established an efficient model deployment and iteration process to ensure that algorithms can be quickly applied to actual products and continuously optimized and upgraded.
Project Experience
E - commerce Platform Product Recommendation System
Algorithm Team Leader
ByteDance
2020 . 032020 . 12
- Project Background: Build an accurate product recommendation system for an e - commerce platform to improve user shopping experience and platform sales.
- Technical Solution: Use deep learning models (such as DeepFM), integrate multi - source data such as user behavior data and product attribute data for personalized recommendation.
- Project Results: After the model was launched, the recommendation click - through rate increased by 15%, and the user purchase conversion rate increased by 10%, bringing significant commercial value to the platform.
Medical Imaging Disease Auxiliary Diagnosis Model
Core Algorithm Member
ByteDance
2021 . 012021 . 12
- Project Background: Develop a disease auxiliary diagnosis model for medical imaging data (such as X - ray and CT images).
- Technical Solution: Use convolutional neural networks (CNN) for image feature extraction and classification, and combine transfer learning to improve the performance of the model on small - sample data.
- Project Results: On the internal test data set, the disease diagnosis accuracy reached 90%, providing an effective auxiliary diagnosis tool for doctors.
Honor Awards
Company Annual Excellent Employee
Technology Innovation Award
Other Information
Open - source Contributions
- Open - sourced multiple machine learning - related code repositories on GitHub, such as the image classification template code based on PyTorch, which received hundreds of Stars and Forks.
- Actively participated in open - source community communication, answered technical questions of other developers, and enhanced personal technical influence.
