Top Deep Learning Jobs in Austin, TX

Lead design and implementation of scalable communication technologies for AI and HPC. Identify performance bottlenecks, co-design HW/SW, build proofs-of-concept, run experiments and simulations at large GPU-cluster scale to accelerate deep learning and HPC workloads.

Develop and optimize a deep learning compiler stack (using LLVM/MLIR) that lowers neural networks from frameworks (PyTorch/TensorFlow/Caffe) to efficient code for embedded/special-purpose platforms, implement NN operation kernels, create optimization and scheduling strategies for multicore/DSP/accelerators, benchmark performance, and collaborate with hardware designers and partners.

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

Design, optimize, and deploy deep learning models for customer behavior prediction. Perform feature engineering, implement research-informed architectures, scale training pipelines, collaborate with client teams, document findings, and uphold engineering best practices.

Design, develop, and deploy state-of-the-art deep learning models for multimodal and satellite imagery; own ML architecture, infrastructure, model registries, and inference services; mentor the team, translate product vision into technical roadmaps, and integrate research into production-ready, scalable geospatial AI systems.

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

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.