Senior Deep Learning Engineer

We are seeking a highly skilled Senior Deep Learning Engineer to drive the development and deployment of advanced perception models for Advanced Driver Assistance Systems (ADAS).

The successful candidate will play a key role in designing cutting-edge neural network architectures, optimizing model performance, and ensuring reliable deployment on embedded platforms. This position requires a balance of deep technical expertise, strong analytical thinking, and cross-functional collaboration.

This role would sit in Mountain View, CA with PhantomAI, Harbinger's ADAS division.

Responsibilities

• Design and implement advanced deep learning architectures to enhance perception capabilities within ADAS systems.
• Maintain and continuously improve existing models by optimizing performance, addressing issues, and refining architecture and algorithms.
• Perform detailed root cause analysis of production issues and develop sustainable, high-quality solutions.
• Optimize model performance with a focus on latency, efficiency, and resource utilization for real-time embedded deployment.
• Integrate and validate deep learning algorithms on automotive-grade hardware and embedded SoCs.
• Collaborate closely with data engineering, data annotation, and platform engineering teams to ensure smooth data flow and seamless model integration.
• Provide regular updates and technical reports on model development, maintenance progress, and performance metrics to management.

Required Qualifications

• 5+ years of professional experience developing, training, validating, and deploying deep learning-based perception models for ADAS or related computer vision applications.
• In-depth understanding of training and inference pipelines, including data loading, augmentation, and loss function design.
• Advanced degree (M.S. or Ph.D.) in Computer Vision, Robotics, Machine Learning, or a closely related discipline, or equivalent industry experience.
• Strong proficiency in Python and a deep understanding of software design principles and development best practices.
• Expertise in PyTorch (preferred) or TensorFlow for large-scale model development and experimentation.
• Practical experience with data pipelines, distributed training, and machine learning experiment management tools.
• Proven ability to work effectively in a collaborative, cross-functional team environment.

Preferred Qualifications

• Comprehensive understanding of machine learning algorithms, including classification, regression, and clustering methods.
• Experience deploying and optimizing models for embedded or automotive SoCs (e.g., NVIDIA Drive, TI TDA4, Qualcomm Snapdragon).
• Proficiency in model optimization techniques such as quantization, pruning, and knowledge distillation.
• Doctorate (Ph.D.) in Computer Science, Artificial Intelligence, or related field is a plus.
• Strong programming experience in Python and/or C++ within Linux development environments.
• Familiarity with automotive perception workflows, datasets, and evaluation frameworks (e.g., KITTI, Waymo, Euro NCAP)