Quantitative Research Platform Engineer - Founding Technical Hire

About the Role

Join a cutting-edge systematic investment firm headquartered in Dubai as their inaugural hire for the quantitative research and engineering team. This pivotal position involves developing and owning a sophisticated distributed research platform that supports large-scale experimental workloads across various assets, models, and timeframes. The platform emphasizes correctness, statistical integrity, and reproducibility as intrinsic features rather than afterthoughts.

Key Responsibilities

• Develop a distributed research engine capable of running thousands of simultaneous backtests and optimisation tasks leveraging elastic, Spot-capable Google Cloud Platform compute resources.
• Implement core platform features including task scheduling, retry mechanisms, failure recovery, monitoring, and cost management.
• Design and maintain deterministic and version-controlled research datasets with a feature computation and caching system ensuring data accuracy including timestamps, historical universes, corporate actions, and data lineage.
• Enable comprehensive parameter and model searches incorporating research validity principles such as walk-forward evaluation, embargoed splits, leakage prevention, transaction-cost sensitivity, and evaluation methods accounting for multiple testing.
• Create a model composition layer to transition from single signal research to integrated portfolio-level strategy simulations.
• Ensure full experiment reproducibility by tracking all results back to exact code, containers, data inputs, features, parameters, and validation configurations through a declarative strategy specification process shared between research and production environments.
• Collaborate directly with the firm's founder to influence platform architecture, experiment representation, execution, and validation.

Requirements

• 4 to 7 years of professional experience building or leading infrastructure for quantitative research, backtesting, simulation, or extensive numerical experimentation, preferably at systematic funds, proprietary trading, high-frequency trading firms, or established crypto trading entities.
• Proven expertise in production-grade Python development including substantial experience with numerical and data engineering libraries such as NumPy, pandas or Polars, and efficient data formats like Apache Arrow or Parquet.
• Significant experience working with distributed or batch compute platforms on cloud providers like Google Cloud Platform (BigQuery, GCS, Batch, Vertex AI) or AWS, applying these to large-scale computational challenges.
• Comprehensive understanding of quantitative research correctness concepts including but not limited to look-ahead bias, survivorship bias, walk-forward validation, overfitting, and transaction cost modeling.
• Professional knowledge of C# preferred to engage with the firm's backtesting engine; alternatively, demonstrated ability to extend or debug sizeable codebases in Java or C++ can be acceptable.
• Experience with large-scale hyperparameter optimization tools such as Optuna or Ray Tune is a strong advantage.
• Skill in designing declarative specifications to orchestrate complex pipelines or experimental configurations.
• Willingness to relocate to Dubai and work onsite.

Compensation and Benefits

• Annual base salary ranging from AED 456,000 to AED 480,000 (approximately USD 124,000 to USD 131,000), paid tax-free.
• Target bonus potential between 20% to 40% of base salary.
• Phantom equity granted after 180 days of employment.

Additional Information

While this role involves deep technical ownership of the quantitative research platform, it is not oriented towards DevOps, Site Reliability Engineering, general cloud platform engineering, or traditional quantitative research. Candidates are expected to engage in greenfield development with significant influence on platform direction, collaborating closely with company leadership. The platform is expected to enter production within six months and become standard across multiple asset classes within a year.

The interview process includes multiple stages: an initial conversation, a rigorous C# technical deep-dive with the founder featuring a source code reading exercise, a live working session focusing on platform development, and a final discussion regarding vision, work style, and compensation. All discussions are confidential with company identity revealed post initial engagement.