Develop and deploy 2D traffic sign detection models for autonomous driving. Responsibilities include scenario analysis, dataset curation, model training and evaluation, optimization and quantization, building evaluation pipelines, long-tail data mining, and validating performance on edge/onboard platforms while collaborating across data, infrastructure, and deployment teams.
XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.
About the RoleWe 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.
- 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.
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.
- Work on production of autonomous driving perception systems with real-world impact.
- 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.
- Gain hands-on experience across the full model lifecycle: from data and training to evaluation, optimization, quantization, and onboard deployment.
What do we provide
- 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.
- Snacks, lunches, dinners, and fun activities.
The base salary range for this full-time position is $215,280 - $364,320, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.
XPeng Motors Palo Alto, California, USA Office
Palo Alto, CA, United States, 94301
Similar Jobs
Artificial Intelligence • Cloud • Machine Learning • Mobile • Software • Virtual Reality • App development
Design, build, and optimize large-scale ML infrastructure: embedding generation, batch inference, data storage/compute, data management, quality systems, and production deployments with ML engineers to improve ranking and recommendation systems.
Top Skills:
C++Embedding SystemsFeature StoreFlinkJavaPythonPyTorchRayScalaSparkTensorFlow
Blockchain • eCommerce • Fintech • Payments • Software • Financial Services • Cryptocurrency
Lead development and production of underwriting and credit decisioning models across Cash App Borrow and Afterpay. Own full modeling lifecycle: problem formulation, feature engineering, training, calibration, experimentation, deployment, monitoring, and iteration. Build decision frameworks, agentic engineering workflows, and collaborate with cross-functional partners to align model behavior with business and regulatory goals.
Top Skills:
AirflowAWSClaude CodeCopilotCursorFeature StoreGCPGitLightgbmMlflowModel Hosting PlatformNumpyPandasPrefectPythonPyTorchScikit-LearnSnowflakeSQLXgboost
Blockchain • Fintech • Mobile • Payments • Software • Financial Services
Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.
Top Skills:
AirflowAWSClaude CodeCopilotCursorGCPGitInternal Feature StoreLightgbmMlflowModel Hosting PlatformNumpyPandasPrefectPythonPyTorchScikit-LearnSnowflakeSQLXgboost
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
