Machine Learning, Cnn, Deep Learning, Tensorflow, Rnn, Pytorch, Python, Transformer, LSTM, ViT, MxNet, Caffe, Model Compression

Jax, Pytorch, Cuda, Python, Docker, Git, Gradient synchronization, PaddlePaddle, Attention mechanisms, MindSpore, MLflow, Model parallelism, Distributed training systems, DeepSpeed, Jupyter, Hugging Face Transformers, Transformer architectures, TensorBoard, Megatron-LM

Pytorch, Cuda, Python, LLM and foundation models

Machine Learning, Unsupervised Learning, Python, agentic AI, adversarial machine learning, debugging complex ML failure modes, reverse engineering model behaviors, supervised learning, generative models, reinforcement learning, data poisoning, ML safety, evaluating frontier AI systems, prompt injection, ML interpretability, multi-object optimization, adversarial robustness, model backdoor detection, adversarial machine learning AI agent security, red-teaming frontier AI systems

alpha generation , Jax, Pytorch, Execution, pre-training large foundation models, transfer across instruments, self-supervised objectives, evaluation protocols, model-serving components, market making, data efficiency, large-scale market datasets, time-series foundation models, tokenization schemes, model architectures, data representations, distributed training recipes, risk-management tasks, scaling laws, regime robustness, training checkpointing, Pricing

Python, Pytorch, Tensorflow, Jax, Deep Learning, diffusion models, autoregressive generative models, video synthesis, generative AI

Pytorch, Cuda, Python, LLM and foundation models