Staff Machine Learning Engineer

About the Role

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.

Basic Qualifications

• 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.

Preferred Qualifications

• 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

• 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.

Why Join Us

• 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.

• 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.