Quantitative Trading & Research - AI Scientist, Quantitative Research - Associate
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What you'll do
- Pre-train Transformer-based and time-series foundation models from scratch using large-scale market, order-book, transaction, and cross-asset datasets.
- Develop data representations, tokenization schemes, self-supervised objectives, model architectures, and distributed training recipes for financial time series.
- Fine-tune and post-train foundation models for alpha generation, pricing, market making, execution, and risk-management tasks.
- Study scaling laws, transfer across instruments and asset classes, regime robustness, data efficiency, and the trade-offs among model quality, inference cost, and latency.
- Design evaluation protocols that connect pre-training metrics to economically meaningful outcomes, including out-of-sample prediction, simulated trading, transaction costs, capacity, and live markouts.
- Build reusable training, checkpointing, evaluation, and model-serving components with ML infrastructure engineers.
What they're looking for
- Advanced degree (Master’s, PhD, or equivalent experience) in machine learning, computer science, statistics, mathematics, operations research, engineering, or a related quantitative field.
- At least 2 years of relevant experience
- Demonstrated experience pre-training a large model from scratch (Transformer/LLM/multimodal/time-series). Experience limited to API usage or prompt engineering is not sufficient.
- Experience building large-scale data pipelines and distributed training systems using PyTorch, JAX, or equivalent frameworks.
- Deep knowledge of large-model training and evaluation: optimization, parallelism, mixed precision, checkpointing, experiment design, ablations, and benchmarking.
- Evidence of research/technical quality through successful large-model training, high-impact research, open-source systems, or production deployment.
Nice to have
- Experience with fine-tuning/post-training for forecasting, ranking, decision-making, or structured prediction.
- Prior 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—especially with models deployed to live trading.
- Publications at leading ML venues and/or substantial contributions to large-scale model-training systems.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
The Quantitative Trading & Research (QTR) group is responsible for systematic trading across FX, Rates, Commodities, Credit, Equity and a wide range of markets. Within QTR, AI Market Lab brings together quantitative research, modern artificial intelligence, market microstructure, and high-performance engineering to develop the next generation of electronic trading capabilities. Our work spans signal research, pricing, market making, execution, portfolio construction, risk management, and the production systems that support them.
We are seeking an AI/ML quantitative researcher with hands-on experience pre-training large foundation models from scratch. You will lead research on building Transformer-based and time-series foundation models over large-scale market datasets, and develop the methods needed to make them robust, transferable, and measurable across instruments and regimes.
This role is designed for someone who wants to do deep research with real constraints—where questions like scaling laws, data efficiency, and robustness are not academic footnotes, but the core of the agenda.
Job Responsibilities
- Pre-train Transformer-based and time-series foundation models from scratch using large-scale market, order-book, transaction, and cross-asset datasets.
- Develop data representations, tokenization schemes, self-supervised objectives, model architectures, and distributed training recipes for financial time series.
- Fine-tune and post-train foundation models for alpha generation, pricing, market making, execution, and risk-management tasks.
- Study scaling laws, transfer across instruments and asset classes, regime robustness, data efficiency, and the trade-offs among model quality, inference cost, and latency.
- Design evaluation protocols that connect pre-training metrics to economically meaningful outcomes, including out-of-sample prediction, simulated trading, transaction costs, capacity, and live markouts.
- Build reusable training, checkpointing, evaluation, and model-serving components with ML infrastructure engineers.
Required Qualifications
- Advanced degree (Master’s, PhD) in machine learning, computer science, statistics, mathematics, operations research, engineering, or a related quantitative field.
- At least 2 years of relevant experience
- Demonstrated experience pre-training a large model from scratch (Transformer/LLM/multimodal/time-series). Experience limited to API usage or prompt engineering is not sufficient.
- Experience building large-scale data pipelines and distributed training systems using PyTorch, JAX, or equivalent frameworks.
- Deep knowledge of large-model training and evaluation: optimization, parallelism, mixed precision, checkpointing, experiment design, ablations, and benchmarking.
- Evidence of research/technical quality through successful large-model training, high-impact research, open-source systems, or production deployment.
Preferred Qualifications
- Experience with fine-tuning/post-training for forecasting, ranking, decision-making, or structured prediction.
- Prior 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—especially with models deployed to live trading.
- Publications at leading ML venues and/or substantial contributions to large-scale model-training systems.
Company
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