Machine Learning Engineer

## Responsibilities * Play a pivotal role in building systems that drive the training and deployment of large-scale ML models across global operations. * Collaborate with leading researchers, hardware experts, and software engineers to build robust solutions that maximize the potential of GPU acceleration, distributed computing, and the latest open-source tools. * Influence trading strategies by accelerating experimentation cycles that foster continuous innovation and refinement. * Develop large-scale distributed training pipelines to manage datasets and complex models. * Build and optimize low-latency inference pipelines, ensuring models deliver real-time predictions in production systems. * Develop libraries to improve the performance of machine learning frameworks. * Maximize performance in training and inference using GPU hardware and acceleration libraries. * Design scalable model frameworks capable of handling high-volume trading data and delivering real-time, high-accuracy predictions. * Collaborate with quantitative researchers to automate ML experiments, hyperparameter tuning, and model retraining. * Partner with HPC specialists to optimize workflows, improve training speed, and reduce costs. * Evaluate and roll out third-party tools to enhance model development, training, and inference capabilities. * Dig into the internals of open-source ML tools to extend their capabilities and improve performance. ## Requirements * 5+ years of experience in machine learning with a focus on training or inference systems. * Strong engineering skills, including Python, CUDA, or C++. * Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or JAX. * Proficiency in GPU programming for training and inference acceleration (e.g., CuDNN, TensorRT). * Experience with distributed training for scaling ML workloads (e.g., Horovod, NCCL). * Exposure to cloud platforms and orchestration tools. ## Nice to Have * Hands-on experience with real-time, low-latency ML pipelines in high-performance environments. * A track record of contributing to open-source projects in machine learning, data science, or distributed systems. ## Benefits * Opportunity to be in a brand new position in a growing team. * Unique opportunity to solve problems at the intersection of advanced machine learning and trading. * Collaborate with a world-class technology backbone and cutting-edge research environment. * Part of a uniquely collaborative, high-performance culture with a commitment to giving back.