AI native quant researcher

We’re hiring for an AI-native quant researcher: a role that blends modern ML research, GPU-heavy experimentation, quantitative modelling, and production-quality engineering. You’ll join a newly formed quant research team whose goal is to build AI-based alpha models that are predictive, reliable and efficient. You will work at the intersection of research and engineering to design, implement, and scale the AI models and algorithms that power our predictive signals. You’ll collaborate closely with data teams, the ML platform team, portfolio teams, and quant researchers across the firm., * You’ll be engaged as a core contributor to our AI-based alpha models, working across research and engineering.

• Research: Iterate on data pipelines, target definitions, model architectures, and training recipes (optimization strategies and hyperparameters). You’ll be responsible for both getting things to work, and developing a deeper understanding, which we can bring to the next problem.
• Engineering: Develop, optimize, and scale distributed training and evaluation workflows for large-scale foundation models, working closely with the ML platform team to efficiently leverage multi-GPU infrastructure.
• Keep up with the latest DL research and collaborate with diverse teams, including other quant researchers, software engineers, and hardware architects.
• Attend conferences and communicate research results to the rest of the firm. Where possible, there will be the opportunity to publish your research.

• Proficiency in Python and at least one deep learning framework such as PyTorch.
• PhD in Computer Science, Machine Learning or other quantitative domains preferred. Open also to candidates with 2/3 years experience in a AI research role.
• Experience with end-to-end model development, spanning dataset construction, training, evaluation, profiling, and monitoring.
• Familiarity with modern model architectures, e.g. MoEs, long-context transformers, vision-language models, efficient attention mechanisms (e.g. GQA/MQA), and new techniques such as multi-token prediction.
• Experience training and scaling models using distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, or Megatron-LM.
• Strong engineering skills, ability to contribute performant & maintainable code, profile bottlenecks, debug training failures, and work with large codebases.
• Initiative and appetite for helping shape the direction of a newly formed team.

Preferred (we encourage you to apply even if you don’t satisfy all of the below):

• Experience optimizing LLM inference (e.g. KV-cache management, continuous batching, quantization, speculative decoding)
• Familiarity with distributed execution and orchestration tools such as Ray or Kubernetes.
• Experience with RLHF, RLAIF, DPO, or reward modeling.
• Experience with CUDA, including developing custom kernels or other GPU performance optimizations.

Founded in 1991, we are a global quantitative and systematic asset management firm applying a scientific approach to finance to develop alternative investment strategies that create value for our clients. We value innovation, dedication, collaboration, and the ability to make an impact. Together, we create a stimulating environment for talented and passionate experts in research, technology, and business to explore new ideas and challenge existing assumptions.