Top Deep Learning Jobs in Austin, TX

Develop deep learning and 3D computer vision models for state estimation, visual navigation, localization, depth, correspondence, and pose estimation. Own data preparation, training, evaluation, benchmarking, and reproducible workflows. Integrate learned measurements into VIO and terrain-relative navigation systems, optimize models for onboard compute, and collaborate with deployment, software, systems, and flight-test teams to deliver production-ready Hivemind SDK components.

Develop and prototype high-performance CUDA-based deep learning systems and custom kernels. Architect and optimize distributed, cluster-scale training and inference pipelines, analyze hardware-software performance bottlenecks, build profiling and runtime tools, and collaborate with researchers, compiler and driver teams to transition prototypes into frameworks or products.

Conduct research and develop algorithms for real-time video processing and deepfake detection, translating machine learning models for production use.

Lead design and training of large-scale transformer and hybrid foundation models across text and multimodal domains. Own architecture choices, diagnose large-scale training instabilities, optimize scaling tradeoffs (data, compute, model), design loss and alignment strategies, and execute distributed training (FSDP/ZeRO, tensor/pipeline parallelism) with mixed precision to build in-house foundation model expertise.

Design and implement novel neural network architectures for YOLO, run large-scale experiments and ablation studies, optimize models for accuracy and efficiency (quantization/pruning/distillation), build training and evaluation pipelines, and collaborate with R&D and engineering to productionize open-source models.

Lead the development of deep learning models for email data, optimize models, design evaluation frameworks, integrate products, and mentor junior researchers.