Machine Learning Research Engineer

A leading proprietary trading firm is hiring a Machine Learning Research Engineer to join a major AI initiative applying advanced deep learning to financial markets. You'll work across model research, training and inference, developing approaches to price forecasting and building the infrastructure needed to turn research into practical trading applications.

Qualifications, skills, and all relevant experience needed for this role can be found in the full description below.

The team brings together deep learning expertise and trading knowledge, with particular interest in time series, transformers and scalable ML systems. Researchers and engineers from Big Tech, AI labs and other research-intensive environments are encouraged to apply. Prior trading experience is not required.

What you'll be doing

• Research and develop deep learning models for price forecasting, exploring approaches including transformers and time-series modelling
• Build scalable, robust training and inference pipelines for large datasets
• Explore and extend the internals of open-source deep learning frameworks to improve functionality and performance
• Work closely with researchers, engineers and trading experts to translate research ideas into practical applications
• Run experiments, evaluate results and iterate quickly in a fast-paced research environment
• Develop an understanding of trading systems and the challenges of applying ML to financial markets

What we're looking for

• PhD or equivalent industry experience in machine learning or a related technical discipline
• Strong experience building deep learning models using PyTorch, JAX or TensorFlow
• Strong Python programming skills
• Research involving transformers, time series or other advanced deep learning architectures
• Experience xhyhwjd conducting computationally intensive research on very large datasets
• A collaborative approach to solving complex technical problems

Additional experience valued

• Experience with the JAX ecosystem, including XLA and Flax
• GPU or accelerator programming using CUDA, Triton or Pallas
• Large-scale distributed training
• Expertise in deep learning framework internals
• Contributions to open-source ML projects or publications at conferences such as NeurIPS or ICML
• C++ programming experience