
ADAS Machine Learning Engineer
About the Role
We are looking for a highly skilled Machine Learning Engineer to join our ADAS (Advanced Driver Assistance Systems) and Autonomous Driving (AD) Data team. This role focuses on developing cutting-edge machine learning models that process and fuse multimodal sensor data—camera, LiDAR, radar—for 2D/3D perception and scene understanding. You will help align temporal data, deploy scalable models, and automate pipelines for training and evaluation.
Key Responsibilities
- Design, develop, and deploy machine learning models for sensor fusion, temporal alignment, and perception in ADAS/AD use cases.
- Integrate models into production workflows and debug performance issues related to misalignments, detection accuracy, and false positives.
- Build and maintain pipelines using MLFlow or similar tools to automate training, validation, deployment, and monitoring.
- Work with large-scale data collected from camera, LiDAR, radar, and vehicle telemetry to train and evaluate models.
- Collaborate with annotation, data platform, and front-end teams to ensure seamless integration of perception models.
- Drive improvements in object detection and scene understanding, both in 2D (camera) and 3D (LiDAR/Radar) domains.
- Utilize methods such as Ray Casting, 3D point cloud segmentation, and tracking to enhance detection performance and reduce latency.
- Contribute to optimizing active learning and sampling strategies to improve model generalization across edge cases.
Qualifications
- 2+ years of experience in automotive, robotics, or a related field, specifically in Perception or ML for ADAS/AD.
- Proven experience building models using multimodal sensor data (camera, LiDAR, radar).
- Deep understanding of object detection, sensor fusion, spatial-temporal modeling, and ray casting techniques.
- Proficiency in Python, PyTorch/TensorFlow, and deep learning frameworks.
- Experience with MLFlow, Kubeflow, or similar ML pipeline platforms.
- Hands-on experience with deploying models to production, debugging, and profiling for performance optimization.
- Solid understanding of 3D geometry, transformations, and calibration in the context of autonomous vehicles.
- Familiarity with tools such as ROS, OpenCV, Open3D, and visualization libraries.
Preferred Qualifications
- Experience working with HD maps, semantic segmentation, and tracking in autonomous driving environments.
- Familiarity with AWS/GCP for distributed training and inference pipelines.
- Experience collaborating with front-end and systems teams to integrate perception output into user-facing applications.
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