Your Core Responsibilities:

📍Design and build end-to-end infrastructure for training, evaluation, and productionization of ML models, working closely with our HPC engineers who manage our on-prem compute cluster 📍Influence foundational choices around data access, compute orchestration, experiment tracking, model versioning, and deployment pipelines 📍Partner with quant researchers to accelerate iteration cycles, tighten feedback loops, and bring models from prototype to live trading 📍Work with researchers to adapt and deploy modern architectures — transformers, state-space models, temporal convolutions, graph neural networks — to noisy, high-frequency financial data. Explore techniques like self-supervised pretraining, representation learning, and cross-sectional modelling where they offer genuine edge 📍Shape our approach to reproducibility, continual learning, and production monitoring across a petabyte-scale data environment 📍Define standards that create consistency across teams and geographies; mentor engineers and influence technical culture beyond your immediate work 📍Keep pace with developments in deep learning research and ML infrastructure; bring ideas from academia and industry into how we work — whether that's new architectures, training techniques, or tooling

Your Skills and Experience:

📍8+ years of experience building ML platforms or infrastructure at a leading tech company, research lab, or quantitative firm 📍A track record of designing and owning large-scale training and inference systems — not just contributing, but architecting 📍Deep proficiency in Python, with strong experience in either CUDA or C++ 📍Hands-on expertise with modern deep learning frameworks (PyTorch, TensorFlow, or JAX) and practical experience implementing architectures like transformers, attention mechanisms, or sequence models 📍Strong foundation in deep learning fundamentals: optimization, regularization, loss design, and the trade-offs that matter when training at scale 📍Experience with distributed training at scale (Horovod, NCCL) and GPU optimization (cuDNN, TensorRT) 📍History of deploying models to production with strong observability, reproducibility, and monitoring practices

Looking for your next challenge? The world of crypto offers exciting roles in blockchain development, web3 innovations, and remote opportunities.