Computer Software Jobs 2026 (Now Hiring) – Smart Auto Apply
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Senior/Staff Computer Vision Engineer - Deep Learning Focus
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26-27 SY Teaching Jobs: Technology, Computer Science
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26-27 SY Teaching Jobs: Technology, Computer Science
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26-27 SY Teaching Jobs: Technology, Computer Science
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Computer Support Specialists
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26-27 SY Teaching Jobs: Technology, Computer Science
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Lead Computer Systems Validation (Csv) Engineer (W)
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Job Description
- 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.
- 3–5+ 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.
- 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)
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