Top Computer Vision Jobs in Austin, TX

Define and develop ultra-low-power imaging, ISP, video, and computer vision architectures for Ambiq SoCs. Create architecture specs, power/performance models, and system flows; drive camera image quality strategy; evaluate/integrate imaging and AI IP; define APIs and HALs; collaborate across FPGA, design, verification, and product teams; support customer engagements and contribute to product roadmaps.

As a Senior Machine Learning Engineer, you'll design and develop workflows utilizing vision-language models, manage data pipelines, and collaborate with multiple teams to enhance model accuracy and efficiency.

As a Staff Software Engineer, you will lead the development of computer vision systems for sidewalk inspection, overseeing model lifecycle, system architecture, and mentoring while improving deployment practices.

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

The role involves designing, building, and deploying computer vision models for vehicle damage detection, covering the entire ML lifecycle and collaborating across teams.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Design, develop, and implement perception systems for autonomous surface vessels: build sensing and navigation algorithms, create metrics for performance evaluation, and work with large datasets for model training and evaluation.

The Perception Engineer will lead the design and deployment of the perception pipeline for detecting and tracking aerial threats using machine learning and computer vision technologies.

Build and maintain scalable image/video data pipelines and curation strategies to optimize datasets for computer vision model training. Implement ingestion, synthetic-data workflows, QA, dataset versioning, metadata/cataloging, labeling coordination, and data-quality tooling to maximize model performance.

Develop, optimize, and deploy production-grade computer vision models (depth, segmentation, tracking) for an autonomous mower. Own model lifecycle from data collection and synthetic-data strategies through training, edge optimization (quantization, mixed precision), and embedded NVIDIA GPU deployment. Integrate perception with robotics stack, analyze field failure cases, and continuously improve robustness for challenging outdoor conditions.