Core Viewpoint - The article discusses the recruitment process and qualifications for a dynamic target perception algorithm engineer in the autonomous driving industry, highlighting the importance of various technical skills and experience in sensor fusion and deep learning [4][6][8]. Group 1: Job Responsibilities - The role involves processing large amounts of autonomous driving data, building automated ground truth labeling systems, and designing cutting-edge AI and vision technologies [6]. - Responsibilities include detecting static scene elements like lane lines and traffic signs, tracking dynamic targets, and predicting the future trajectories and intentions of moving objects [8]. - The engineer will work on multi-sensor fusion, depth estimation, and developing calibration methods for various sensors [8]. Group 2: Qualifications - Candidates should have a master's degree in computer science, automation, mathematics, or related fields, with experience in perception algorithms for autonomous driving or ADAS systems being a plus [6]. - Proficiency in programming languages such as C++ or Python, along with solid knowledge of algorithms and data structures, is required [8]. - Familiarity with multi-view geometry, computer vision technologies, deep learning, and filtering and optimization algorithms is essential [8]. Group 3: Community and Learning Resources - The article mentions a community of nearly 4,000 members and over 300 autonomous driving companies and research institutions, providing a comprehensive learning path for various autonomous driving technologies [9]. - Topics covered include large models, end-to-end autonomous driving, sensor calibration, and multi-sensor fusion [9].

Core Viewpoint - The article discusses the recruitment process and job responsibilities for a perception algorithm engineer in the autonomous driving industry, emphasizing the importance of skills in computer vision, deep learning, and sensor fusion technologies [1][5][6]. Group 1: Job Responsibilities - The role involves processing large amounts of autonomous driving data, building automated ground truth labeling systems, and designing cutting-edge AI and vision technologies [6]. - Algorithms and code developed will be deployed in millions of mass-produced vehicles [6]. - Key tasks include detecting static scene elements, tracking dynamic targets, and developing calibration methods for various sensors [10]. Group 2: Job Qualifications - Candidates should have a master's degree or higher in relevant fields such as computer science, automation, or mathematics [7]. - Proficiency in programming languages like C++ or Python, along with solid knowledge of algorithms and data structures, is required [7]. - Familiarity with multi-view geometry, computer vision, deep learning, and sensor technology applications is essential [7]. Group 3: Preferred Qualifications - Experience in developing perception algorithms for autonomous driving systems or ADAS, such as lane detection and obstacle tracking, is a plus [9]. - Candidates with experience in sensor fusion involving visual, LiDAR, and millimeter-wave radar are preferred [9]. - Publications in top conferences or journals in the fields of computer vision, machine learning, or robotics are advantageous [9]. Group 4: Community and Resources - The article mentions a community platform for job seekers in autonomous driving and robotics, providing resources such as interview questions, industry reports, and salary negotiation tips [12][13]. - The community aims to assist members in preparing for job applications and understanding industry trends [12][21].

Core Viewpoint - Didi has a strong technical foundation in the autonomous driving sector, particularly in perception algorithms, and is a key player for those interested in pursuing careers in this field [2]. Group 1: Interview Process - The interview process for the perception algorithm position at Didi consists of three technical rounds, with a focus on project details and technical principles [2]. - Candidates are advised to thoroughly understand every detail on their resumes, as interviewers may ask in-depth questions [2]. Group 2: Technical Questions - The first round includes self-introduction and targeted questions about the candidate's research output and direction [3]. - Candidates are asked to explain the core innovations of their papers, leading to discussions on 2D object detection [4]. - The evolution of 2D object detection algorithms from traditional methods to deep learning is a key topic [5]. - Understanding of Anchor-Free detection, specifically the core process of the FCOS algorithm, is assessed [6]. - Candidates are questioned about their familiarity with end-to-end detection algorithms, reflecting the latest developments in the field [7]. - The mechanism of DETR in achieving end-to-end object detection is explored in depth [8]. Group 3: Project Experience - Candidates are expected to present their project experiences, such as a perception project based on the BEVDet model, detailing the algorithm architecture and detection process [9]. - Interviewers inquire about specific challenges faced during the implementation of algorithms in real-world applications and the solutions devised [10]. Group 4: Coding Assessment - A coding challenge is included, where candidates must write the NMS (Non-Maximum Suppression) post-processing code on-site [11]. Group 5: Community and Networking - A community has been established for job seekers in autonomous driving and related fields, with nearly 1,000 members from various companies, providing a platform for networking and support [12].