ML Researcher (Time Series / Signals)

ALT Fund; Dubai; On-site; AED 350,000 - 450,000; Full time

Job summary

A leading prop-trading company in Dubai is looking for a Quantitative Researcher to develop innovative machine learning models for forecasting high-dimensional time series. The ideal candidate will have a strong academic background, experience in machine learning, and a passion for research in finance. Join us to make an impact with cutting-edge technology and enjoy a supportive work environment with ample vacation and benefits.

Benefits

35 Days of Vacation

100% Paid Sick Leave

Top-Tier Equipment

Corporate Psychologist

Qualifications

• 4–8 years of work experience, ideally a mix of academia and industry.
• Publications at top AI venues in Time Series or Signal Learning.
• Experience building forecasting models for market or macroeconomics.

Responsibilities

• Research and develop machine learning models for forecasting time series.
• Build end-to-end ML pipelines for data processing and modeling.
• Collaborate with team members to integrate ML models into investment processes.

Skills

Machine Learning

Time Series Modeling

Deep Learning

Data Ingestion

Model Calibration

Education

Master’s degree or PhD in a quantitative field

Tools

PyTorch

Docker

Job description

We are a prop-trading company that combines the agility of a startup with the resources of a high-performing fund. Our team is focused on developing cutting-edge strategies, and working with us means not just advancing technology, but also being part of a team where ideas are valued, professional growth is encouraged, and every member has the opportunity to unlock their full potential.

We’re looking for a Quantitative Researcher with a strong background in machine learning and time series modeling to join our team.

What You’ll Be Doing :

• Researching, developing, and deploying cutting-edge machine learning models for forecasting complex, high-dimensional time series — from market signals to macroeconomic indicators and alternative data.
• Building ML pipelines from scratch : data ingestion, feature processing, modeling, calibration, and monitoring.
• Designing custom validation and testing approaches for non-stationary data, including regime shift detection and adversarial evaluation.
• Working with large-scale data sources — tick-level, satellite, transactional, web-scraped — and transforming them into structured features.
• Collaborating with quants and engineers to integrate ML models into real-world investment processes.
• Contributing to strategic research initiatives, including causal inference, representation learning, and attention-based models for time series.

Requirements

Experience :

• 4–8 years of work experience, ideally a mix of academia and industry.
• Publications at top AI venues (NeurIPS, ICLR, ICML) in the fields of Time Series or Signal Learning.
• Experience building models that forecast market or alternative signals, macroeconomics, commodities, or sentiment.
• Participation in building an ML research culture : internal toolkits, mentorship, and open science practices.

Skills & Education :

• Expertise in deep learning for time series : Temporal Fusion Transformers, DeepAR, N-BEATS, PatchTST.
• Knowledge of causal inference and counterfactual reasoning for time series.
• Experience in multi-modal learning (time series + tabular data + text).
• Proficiency with the ML stack : PyTorch, HuggingFace, DVC, Docker, etc.
• Ability to build end-to-end ML pipelines — from data ingestion to production inference.
• Master’s degree or PhD in a quantitative field (Physics, Mathematics, Computer Science, or related areas).

Nice to have :

• Understanding of option pricing models, hedging.
• Experience with C++ or Rust.
• Ability to communicate technical ideas to diverse audiences, including non-technical stakeholders.
• Culture of Innovation : An open, dynamic, and inclusive environment where your ideas matter.
• Flexibility & Impact : Enjoy the freedom of a startup with the backing of a well-resourced fund.
• High Impact : Work directly on projects that shape strategies and drive the fund’s success.
• 35 Days of Vacation – Plenty of time to rest and recharge.
• 100% Paid Sick Leave – Recover without financial worries.
• Top-Tier Equipment – Choose the tools that suit you best (within budget).
• Corporate Psychologist – Mental health support when you need it.