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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer / Computer Vision Engineer - **Company:** XPeng Inc. - **Location:** Santa Clara, United States - **Experience:** Experienced - **Salary:** $215,280.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Computer Vision, C++ (Programming Language), Nvidia CUDA, Data Cleansing, Data Mining, Software Debugging, Python (Programming Language), Machine Learning, Object Detection, Regression Testing, Tensorflow, Software Deployment, Pytorch, Delivery Pipeline, Deep Learning, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, TensorRT, Software Version Control, Data Pipelines - **Published:** July 28, 2026 - **Apply:** https://www.dice.com/job-detail/7a8d04c7-ce41-4aaa-8975-ef51b62cbfbb ## About the Role * Master's, or PhD degree in Computer Science, Electrical Engineering, Robotics, Computer Vision, Machine Learning, or a related field. * 3-5 years of strong hands-on experience with computer vision models, especially object detection. * Experience with detection architectures such as YOLO, Faster R-CNN, DETR/Deformable DETR, RT-DETR, RTMDet, or similar models. * Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow. * Solid understanding of object detection training workflows, including dataset preparation, augmentation, loss functions, evaluation metrics, and model debugging. * Experience with common detection metrics such as mAP, precision/recall, false positive/false negative analysis, and class-level performance breakdown. * Strong data analysis and problem-solving skills. * Ability to work cross-functionally with model, data, infrastructure, and deployment teams., * 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. ## Description 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. 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. What You'll Do * Develop and improve 2D traffic sign detection models for autonomous driving perception systems. * Analyze TSR-related scenarios and failure cases, including missed detections, false positives, occlusions, small objects, rare signs, region-specific signs, and adverse weather or lighting conditions. * Prepare, clean, curate, and analyze training and evaluation datasets for TSR model iteration. * 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. * Collaborate with data teams to define mining strategies for long-tail TSR scenarios and improve dataset coverage. * 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. * Debug issues across the full stack, including data quality, labeling, model behavior, evaluation mismatch, and deployment consistency., A successful engineer in this role will: * Improve TSR detection performance across both common and long-tail traffic sign scenarios. * Build reliable data and evaluation workflows to support fast model iteration. * Identify and prioritize high-impact failure modes through scenario analysis and data mining. * Deliver deployable TSR models with strong accuracy, latency, and robust tradeoffs. * Help establish a scalable data-model-evaluation-deployment loop for production TSR development. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Intelligent Data Selection for Continual Learning of AI Functions](https://www.wearedevelopers.com/videos/367-intelligent-data-selection-for-continual-learning-of-ai-functions) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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