Quant jobs / JPMorgan

AI Scientist, Quantitative Research – AI Market Lab

Researcher who leads work on foundation models for financial time series. Sits in J.P. Morgan's AI Market Lab within the systematic trading group.

What you’d work on

Our reading of the posting: what a new hire in this role would most likely spend their first months on.

Pre-train transformer and time-series foundation models on order-book and cross-asset data
Design tokenisation, self-supervised objectives and distributed training set-ups for price series
Fine-tune models for alpha, pricing, market making and execution tasks
Build evaluation that ties model loss to simulated trading outcomes, costs and live markouts

What they ask for

Required

Hands-on pre-training of a large model from scratch
Distributed training in PyTorch, JAX or similar
Large-scale data pipelines
Optimisation, parallelism and mixed-precision training
Experiment design, ablations and benchmarking

Preferred

Fine-tuning and post-training for forecasting or ranking
Time-series foundation models or limit-order-book modelling
Quant trading, HFT or market-making experience
Publications at leading ML venues

Education: Master's, PhD or equivalent experience in ML, computer science, statistics, maths or related field
Experience: Proven large-model pre-training experience; API use or prompting alone does not count
Languages and tools: Python
PyTorch
JAX
Markets: multi-asset

How to prepare

Pre-train a small transformer on public limit-order-book or tick data and track scaling behaviour
Implement distributed training with PyTorch or JAX, including checkpointing and mixed precision
Learn to evaluate forecasts economically: costs, capacity and leakage-free splits
Read recent papers on time-series foundation models and write up a replication

What to study

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View the posting on JPMorgan’s site ↗

SelfMadeQuant wrote this summary from the employer’s public posting, last checked 6 November 2026. SelfMadeQuant isn’t affiliated with JPMorgan and doesn’t handle applications.