Staff Algorithm Engineer

7 days ago

Haidian District Beijing, China QCraft AI Full time

About us:

Founded in 2019,
QCraft
is committed to bringing
autonomous driving
into reality. It focuses on autonomous driving technology and provides partners with mass-produced autonomous driving solutions in complex urban traffic environments.

At present, Qcraft has deployed the Dragon Boat series of autonomous driving vehicles in more than 10 cities in China. The fleet size is nearly
100
vehicles, covering a variety of models, and has carried out regular operations on public roads.

Job Responsibilities:

Responsibilities

  1. Design and innovate end-to-end perception large model architectures, including but not limited to BEVFormer, Occupancy Networks, Nerf-based methods, Language Models for Driving, and other cutting-edge directions;
  2. Lead multi-modal large model training to achieve unified representation learning and fusion inference for multi-sensor data (cameras, LiDAR, radar);
  3. Build data-driven closed-loop training systems, design automated data mining, hard case mining, and model self-iteration strategies to improve generalization on corner cases;
  4. Optimize large model deployment on in-vehicle compute platforms (NVIDIA Orin, Thor, etc.) via quantization, pruning, distillation, ensuring real-time performance and functional safety compliance;
  5. Collaborate with planning & control teams to explore integrated perception-prediction-decision end-to-end architectures, driving technology into production vehicles;
  6. Track latest research from CVPR/ICCV/NeurIPS/ICLR, reproduce and improve SOTA algorithms, write technical documentation and patents.

Requirements

  1. Master's or Ph.D. in Computer Science, Automation, Robotics, or related fields (Ph.D. preferred);
  2. 3+ years of R&D experience in autonomous driving perception or large model algorithms, with at least one complete mass-production project;
  3. Proficient in PyTorch/TensorFlow, familiar with distributed training frameworks (DeepSpeed, Megatron-LM);
  4. Deep understanding of Transformers, Attention mechanisms, and multi-modal learning principles, with hands-on experience in CLIP, SAM, DINOv2, etc.;
  5. Familiar with BEV perception, Occupancy prediction, 3D object detection. Candidates with SOTA results on nuScenes/Waymo datasets are preferred;
  6. Skilled in CUDA programming and model deployment optimization, familiar with TensorRT, ONNX toolchains;
  7. First-author publications in top-tier conferences (CVPR/ICCV/ECCV/NeurIPS) are a strong plus。

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