Role Overview:

We are seeking a Machine Learning Framework Developer to spearhead the development and enhancement of our quantitative trading model frameworks. This role is instrumental in optimizing our proprietary trading algorithms and streamlining our trading processes.

What you'll do:

- Collaborate with the quant team to develop and fine-tune the performance of our model training framework.

- Optimize CPU/GPU inference latency, architect computational pipelines based on trading strategies, and elevate the efficiency of our algorithmic operators.

- Boost data ingestion speeds, maximize cache utility, refine distributed training mechanisms, and ensure optimal GPU resource allocation throughout the model training phase.

- Lead the design and implementation of cutting-edge machine learning training tools, ensuring a seamless transition from model backtesting to live trading. You will also be streamlining the workflow for our researchers in managing trading models, data sets, and complex algorithms.

Requirements:

- Master's or PhD in Financial Engineering, Computer Science, Mathematics, Statistics, or a related discipline.

- Proficiency in C++, CUDA, and Python with a demonstrable track record of robust coding and practical application in a finance/trading environment.

- Comprehensive understanding of Tensorflow or Pytorch, with hands-on experience in distributed training architectures and optimization techniques.

- Familiar with high-frequency trading environments, especially with respect to high-performance RDMA protocols.