Job Description
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 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.
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.
JOB RESPONSIBILITIES
* 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
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.
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.
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.
Job Tags
Full time