Top Computer Vision Jobs in Austin, TX

Summer 2027 intern supporting real-time computer vision and machine learning systems for autonomous counter-drone platforms. Responsibilities include detecting, tracking, and classifying aerial threats; generating training data; building data pipelines; optimizing models for NVIDIA Jetson embedded hardware; and experimenting with sensor fusion, tracking robustness, and field performance. The role involves hands-on collaboration with engineers developing deployed defense systems.

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, train, and optimize computer vision and ML models (particularly video-based safety) using petabyte-scale camera and sensor data. Deploy and optimize models for backend and edge inference, collaborate with cross-functional teams, and translate customer needs into scalable ML/CV solutions.

Develop and deploy production computer vision and machine learning solutions for object detection, segmentation, classification, video analysis, forecasting, and anomaly detection. Responsibilities include selecting and fine-tuning models with PyTorch or TensorFlow, analyzing performance, translating business problems into scalable AI solutions, and accounting for real-world constraints involving cameras, sensors, lighting, and edge-device compute limitations.

Build and improve AI image-editing systems, backend services, product features, training and inference pipelines, model-serving infrastructure, and media-processing workflows. Optimize computer-vision algorithms for speed and accuracy, develop internal tools, debug systems, and ship full-stack features for image editing, virtual staging, video workflows, and a model marketplace. The role requires broad engineering ability plus specialized expertise in areas such as computer vision, machine learning, frontend, or distributed systems.

Design and develop real-time perception software for automated forklifts and loading dock systems. Lead technical efforts, troubleshoot system-level performance issues, define requirements and roadmaps, mentor engineers, and engage customer stakeholders on technical scope and KPIs. Work across perception, controls, architecture, CI/CD, and planning while delivering reliable robotic systems into production.

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.

Review and annotate robotic-system video footage by identifying objects, movements, events, visual features, and anomalies. Provide feedback to improve video datasets and annotation consistency, collaborate with AI teams on annotation protocols, document observations, participate in quality reviews, and recommend improvements to video capture and labeling strategies.

Designs and demonstrates computer vision solutions for customer opportunities, including model selection, data strategy, evaluation, edge deployment, and business-case development. The role leads discovery, high-level architecture, proofs of concept, demos, partner engagement, and technical enablement. It requires hands-on expertise across classical computer vision, deep learning, video intelligence, vision language models, model lifecycle management, and edge optimization, plus strong customer-facing and commercial communication skills.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.