Computer Vision Engineer – Advanced Development & Planning
Job in
Mountain View, Santa Clara County, California, 94043, USA
Listed on 2026-07-04
Listing for:
Artech
Full Time
position Listed on 2026-07-04
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Computer Science
Job Description & How to Apply Below
Computer Vision Engineer
Develop proof-of-concept (POC) computer vision systems for intelligent mobility and in-vehicle applications. The role focuses on rapidly evaluating, implementing, and deploying state-of-the-art computer vision and AI technologies on vehicle-grade edge computing platforms, bridging cutting-edge research and real-world intelligent mobility systems.
RequiredSkills & Qualifications
- Master's degree or higher in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, or a related field.
- Strong background in Computer Vision and Machine Learning.
- Hands-on experience with PyTorch, Tensor Flow, OpenCV, or similar frameworks.
- Experience implementing and training deep learning models including CNNs, Transformers, Vision Transformers (ViT), Vision-Language Models (VLMs), and multimodal architectures.
- Strong programming skills in Python and C++.
- Prior work experience at client or in client's Industry.
- Applicants must be able to work directly for Artech on W2.
Skills & Qualifications
- Experience with Foundation Models, Large Vision Models, and Multimodal AI systems.
- Experience with CLIP, SAM (Segment Anything), DINOv2, BEV-based perception models, or similar state-of-the-art vision technologies.
- Experience with Generative AI and synthetic data generation.
- Develop proof-of-concept systems using state-of-the-art AI and computer vision technologies.
- Design and implement computer vision algorithms for object detection, tracking, semantic/instance segmentation, 3D scene understanding, visual localization, mapping, driver and occupant monitoring, and behavior recognition.
- Build solutions using modern AI approaches including Vision Transformers (ViT), Vision-Language Models (VLMs), Multimodal AI, Foundation Models, Self-Supervised Learning, and Generative AI.
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