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.
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