Job Description

AI - ML, Alpha/Signal Generation, Discretionary Trading & Research, Finance, Quantitative Developer, Systematic Trading & Research
Abu Dhabi, Dubai, Florida, Hong Kong, London, New York, Remote, Singapore, Zug

About Our Client

Our client is a rapidly expanding buy-side firm specializing in systematic trading across derivatives and cash markets. With a strong emphasis on

AI-driven research

, they integrate

deep learning and reinforcement learning

into high-frequency and medium-frequency trading strategies to generate uncorrelated returns for institutional investors.

The Opportunity

Join an elite research team at the forefront of

AI-driven quantitative trading

. This role focuses oning

state-of-the-art deep learning techniques

to options, futures, and structured derivatives markets, with direct exposure to

real-time alpha generation, portfolio optimization, and execution strategies

. You will develop

cutting-edge models

for forecasting market dynamics and optimizing trade execution in a systematic, data-driven framework.

Key Responsibilities

Develop advanced AI/Deep Learning models

for predictive signal generation in derivatives markets (volatility surfaces, term structure forecasting, order flow dynamics).

reinforcement learning

to optimize execution strategies, market making, and hedging frameworks.

Build and refine NLP-based models

for extracting signals from alternative datasets (news sentiment, earnings call transcripts, options order flow).

Enhance systematic options trading strategies

(e.g., delta-hedging, volatility arbitrage, statistical arbitrage) using deep learning-based predictive frameworks.

Deploy and optimize AI models in production

with real-time inference and model adaptation to changing market conditions.

Improve research infrastructure

(scalable data pipelines, high-performance backtesting engines, deep learning model training frameworks).

Collaborate with portfolio managers and execution teams

to integrate AI-driven signals into risk-managed trading portfolios.

Publish internal research

on deep learning architectures for financial time series forecasting, reinforcement learning for derivatives trading, and explainability of AI-driven strategies.

Ideal Candidate Profile

5+ years of experience in quantitative research, systematic trading, or AI-driven signal development

at a top-tier hedge fund, prop firm, or high-frequency trading firm.

Strong track record of alpha generation

in derivatives markets (Sharpe ratio, risk-adjusted returns, and execution efficiency).

Expertise in AI/Deep Learning frameworks:

PyTorch, TensorFlow, JAX, or Hugging Face Transformers.

Strong programming skills in Python and C++

(for low-latency research and execution).

Advanced degree (PhD preferred) in AI, Machine Learning, Quantitative Finance, or Computational Sciences.

Deep knowledge of:

+ Neural networks for time-series forecasting (LSTMs, Transformer models, CNNs for market data).
+

Reinforcement learning for execution optimization

(Q-learning, PPO, AlphaZero-style models).
+

Generative models for synthetic data generation

(GANs, VAEs, diffusion models).
+

Derivatives pricing models

(stochastic volatility, Monte Carlo simulations, local volatility).
+ Market microstructure and high-frequency trading for

listed options and futures

.
+ Portfolio optimization under liquidity, margin, and regulatory constraints.

Why Join?

Be a

key player in an AI-first quantitative research team

, working with top-tier hedge fund PMs and AI researchers. Work with

exclusive datasets

(tick-level options data, alternative data partnerships, deep order book data). Access to

world-class compute resources

for deep learning model training and high-performance AI-driven research. *

Remote-first culture

with flexibility to work from anywhere, while engaging with top-tier talent in AI and systematic trading.