Xpengmotors

Xpengmotors

Staff Machine Learning Engineer

Santa Clara, CA · Staff+

Sponsorship not specifiedDetected 28 days ago
PythonC++Full-Stack DevelopmentMachine LearningDeep LearningPyTorchData AnalysisData EngineeringComputer VisionRoboticsElectrical EngineeringManual TestingProblem Solving

About the role

  • We are looking for a strong Machine Learning Engineer / Computer Vision Engineer to work on Traffic Sign Recognition ( TSR ) 2D detection for production autonomous driving systems.
  • In this role, you will be responsible for the full lifecycle of TSR model development, including scenario analysis, data preparation, model training, evaluation, optimization, quantization, and deployment.
  • You will work closely with perception, data, infrastructure, and deployment teams to improve traffic sign detection performance across diverse real-world driving scenarios.

Responsibilities

  • Develop and improve 2D traffic sign detection models for autonomous driving perception systems.
  • Design and execute model training experiments, including data sampling, augmentation, loss tuning, class imbalance handling, and hard-case mining.
  • Build and maintain evaluation pipelines for TSR models, including offline metrics, scenario-based evaluation, regression testing, and error analysis.
  • Optimize models for production deployment, including ONNX / TensorRT / quantization / inference acceleration.
  • Work with deployment and platform teams to validate model performance on onboard or edge compute platforms.
  • Track model performance across versions and support continuous improvement through data-model-evaluation feedback loops.
  • Build reliable data and evaluation workflows to support fast model iteration.
  • Deliver deployable TSR models with strong accuracy, latency, and robust tradeoffs.
  • Own an important perception task that directly affects driving safety, rule understanding, and product quality.
  • Collaborate with strong teams across model development, data, deployment, and vehicle platforms.

Requirements

  • Experience with detection architectures such as YOLO, Faster R-CNN, DETR/Deformable DETR, RT-DETR, RTMDet, or similar models.
  • Experience with common detection metrics such as mAP, precision /recall, false positive / false negative analysis, and class-level performance breakdown.
  • Ability to work cross-functionally with model, data, infrastructure, and deployment teams.

Nice to have

  • Experience in autonomous driving, ADAS, robotics, or safety-critical perception systems.
  • Experience with traffic sign recognition, traffic light recognition, road object detection, or small-object detection.
  • Familiarity with long-tail scenario mining, hard negative mining, class imbalance handling, and dataset curation.
  • Experience with ONNX, TensorRT, model quantization, C++ inference pipelines, CUDA, or edge deployment.
  • Experience debugging training-to-deployment consistency issues, including preprocessing mismatch, postprocessing mismatch, quantization accuracy drop, or runtime performance bottlenecks.
  • Familiarity with large-scale data pipelines, scenario tagging, or automated data mining workflows.
  • Strong engineering discipline in experiment tracking, reproducibility, regression testing, and model version management.
  • What Success Looks Like

Skills

  • 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 a significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Competitive compensation package.

Compensation

  • Competitive compensation package.

Benefits

  • This role is ideal for candidates who enjoy solving practical computer vision problems, building reliable model iteration pipelines, and bringing perception models from offline training to onboard production systems.
  • Collaborate with data teams to define mining strategies for long-tail TSR scenarios and improve dataset coverage.

This listing is sourced directly from Xpengmotors's careers page and normalized into a canonical job model.