Quant jobs / Akuna Capital

Junior Quantitative Researcher – Deep Learning (Time Series)

An entry-level deep learning research role on large-scale time series at an options market maker's Shanghai office. The work supports trading strategies and portfolio construction.

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.

Train deep learning models on large time-series data
Build trading strategies from statistical and DL methods
Design optimisation methods for portfolio construction
Build data pipelines and scale training

What they ask for

Required

Hands-on deep learning with sequence or time-series data
Strong Python
PyTorch or TensorFlow
Handling large datasets and batching
Solid maths and statistics
Technical interviews in English

Preferred

C++
GPU experience
Time-series issues such as non-stationarity and autocorrelation

Education: Bachelor's, master's or PhD in a technical field; graduated between July 2023 and July 2027
Experience: Recent graduates with hands-on deep learning projects; finance experience not required
Languages and tools: Python
PyTorch
TensorFlow
C++
Markets: options, multi-asset

How to prepare

Train an LSTM or Transformer on a financial time series and compare it to simple baselines
Study non-stationarity and leakage in time-series validation
Practise PyTorch data pipelines for large datasets
Revise optimisation and regularisation theory

What to study

The NLP and ML Quant Researcher roadmap sets out the stages for this kind of role, with free resources for each.

Open the roadmap

Apply

Read the full posting on Akuna Capital’s site before applying: requirements, location and pay can change after we check them.

View the posting on Akuna Capital’s site ↗

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