Top Computer Vision Jobs in Los Angeles, CA

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.

Develop and implement computer vision algorithms for drones, ensuring high performance in real-time autonomous systems. Collaborate with engineering teams for integration.

The role involves developing and deploying computer vision models, collaborating on technical solutions, and utilizing advanced machine learning and AI techniques for Space applications.

Lead computer vision model development and deployment, architect scalable pipelines, integrate hardware with AI monitoring, and ensure efficient manufacturing processes.

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.

Work on vision-language model development and infrastructure: improve VLM understanding, OCR and function-calling workflows, fine-tuning recipes, evaluation and robustness, and optimize training/serving pipelines. Own end-to-end projects from experiments to production-ready code, collaborating on scale, cost, and reliability.