Quantitative Trading & Research – AI/ML Quantitative Researcher – Associate or Vice President

Overview

In this role you drive research on Transformer-based and time-series foundation models for across-asset trading data, shaping robust, transferable AI methods. You will pre-train large models from scratch and tackle data efficiency, robustness, and scaling in a live trading context. You collaborate with ML infra engineers to deploy scalable training and model-serving pipelines. This position sits at the AI Market Lab within QTR, combining quantitative research with cutting-edge market AI to enhance alpha generation, pricing, and execution. You’ll work on meaningful problems that connect research to observable trading outcomes and risk management.

Responsibilities

• Pre-train Transformer-based and time-series foundation models from scratch on large-scale market datasets
• Develop data representations, tokenization, self-supervised objectives, architectures, and distributed training recipes for financial time series
• Fine-tune and post-train models for alpha generation, pricing, market making, execution, and risk management
• Study scaling laws, cross-instrument transfer, regime robustness, and trade-offs between model quality, inference cost, and latency
• Design evaluation protocols linking pre-training metrics to economic outcomes (out-of-sample prediction, simulated trading, costs, capacity, live markouts)
• Build reusable training, checkpointing, evaluation, and model-serving components with ML infrastructure engineers

Key requirements

• Advanced degree (Master’s, PhD, or equivalent) in ML, CS, statistics, mathematics, OR engineering or related quantitative field
• Experience pre-training a large model from scratch (Transformer/LLM/multimodal/time-series); API usage or prompt engineering alone is not sufficient
• Experience building large-scale data pipelines and distributed training with PyTorch, JAX, or equivalent
• Deep knowledge of large-model training and evaluation: optimization, parallelism, mixed precision, checkpointing, ablations, benchmarking
• Evidence of research/technical quality via successful large-model training, high-impact research, open-source systems, or production deployment

• Transformer/LLM/time-series
• PyTorch
• JAX
• large-scale data pipelines
• distributed training
• checkpointing

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