DeepL★3.8 (4)· London

You will build and deploy state-of-the-art reinforcement learning pipelines at scale using Python, PyTorch, TensorFlow, or JAX, working with large compute clusters and ML infrastructure. As part of DeepL’s Foundation Model Task Adaptation team, you will post-train large (multi-modal) models to align them with human intent and enable capabilities like reasoning.

Jump Trading★3.8 (11)· London

Use Python, C++, PyTorch, TensorFlow, and JAX (plus CUDA/ROCm for HPC) to apply machine learning and deep learning research. The team of traders, quantitative researchers, and engineers uses statistical analysis and data mining to develop predictive trading models for global financial markets.

Jump Trading★3.8 (11)· London

Jump Trading seeks PhD/postdoc research scientists to apply machine learning and deep learning (transformers, SSMs) using Python, C++, PyTorch, TensorFlow, or JAX. The role involves conducting and implementing open-ended research projects to develop predictive trading models for the firm’s global financial market operations.

Hudson River Trading★4.5 (2)· London

You'll work on deep learning and foundation models that ingest market data to predict future market states, using GPU clusters with support for PyTorch, JAX, C++, and FPGA/ASIC development. Hudson River Trading's HAIL team develops and deploys these models directly for its trading operations, with researchers having significant autonomy within a small, focused team to improve all aspects from data featurization through architecture design to trading decisions.