Quantitative Trading & Research - AI Scientist - Associate

What you'll do

• Pre-train Transformer-based and time-series foundation models on large-scale market datasets, and develop their data representations, objectives, architectures, and distributed training methods.
• Fine-tune and evaluate models for alpha generation, pricing, market making, execution, and risk management, including testing robustness, transfer, and economic outcomes.
• Build reusable training, evaluation, checkpointing, and model-serving components with ML infrastructure engineers.

What they ask for

• Advanced degree (Master’s, PhD, or equivalent experience) in a quantitative field such as machine learning, computer science, statistics, mathematics, operations research, or engineering.
• Demonstrated experience pre-training a large model from scratch; API usage or prompt engineering alone is insufficient.
• Experience building large-scale data pipelines and distributed training systems using PyTorch, JAX, or equivalent frameworks.
• Deep knowledge of large-model training and evaluation; evidence of technical quality through successful training, research, open-source systems, or production deployment.

Nice to have

• Experience with fine-tuning or post-training for forecasting, ranking, decision-making, or structured prediction; or work on time-series foundation models, limit-order-book modeling, multimodal market data, or cross-asset transfer learning.
• Experience in quantitative trading, HFT, electronic market making, or systematic investing; publications at leading ML venues or substantial contributions to large-scale model-training systems.

Pay

USD $150,000.00-$200,000.00 base salary

Tools

Transformer, PyTorch, JAX

AI focus

Pre-training and adapting foundation models for financial time series and trading tasks, with research on scaling, data efficiency, transfer, robustness, and economically meaningful evaluation.