ML Quant Researcher/Developer

About the Opportunity

A highly collaborative and entrepreneurial systematic macro trading team is seeking a Quantitative Developer with expertise in machine learning engineering to help build next-generation research and trading capabilities. The team operates within a leading global alternative investment platform known for its significant investment in technology, quantitative research, and data-driven decision making.

This is a unique opportunity to sit at the intersection of quantitative research, machine learning, software engineering, and systematic trading. The successful candidate will work directly with senior investment professionals to develop sophisticated machine learning models, scale research infrastructure, and accelerate the deployment of predictive signals into live trading environments.

Unlike traditional software engineering roles, this position offers direct exposure to the investment process and the opportunity to influence trading performance through improvements in data science workflows, computational infrastructure, and model development frameworks. The individual will play a critical role in enabling researchers to test ideas more efficiently, explore larger parameter spaces, and deploy production-ready machine learning models on high-frequency financial datasets.

The Role

The Quantitative Developer will focus on designing, building, training, optimizing, and productionizing machine learning systems that operate on large-scale financial data. The role spans the entire model lifecycle from data ingestion and feature engineering through training, validation, deployment, monitoring, and ongoing enhancement.

The successful candidate will work closely with quantitative researchers and portfolio managers to transform research concepts into robust production systems capable of operating in demanding, performance-sensitive environments. This includes building scalable research platforms, enhancing distributed computing capabilities, improving experimentation workflows, and ensuring seamless migration from research prototypes to production trading systems.

The position is ideally suited to individuals who enjoy solving complex engineering problems, working with cutting-edge machine learning techniques, and applying advanced computational methods to highly challenging real-world datasets.

Key Responsibilities

Machine Learning Development

• Design, develop, train, test, and deploy machine learning models using both classical and deep learning methodologies.
• Build predictive models using large-scale, high-frequency datasets.
• Improve model training pipelines to support rapid experimentation and deployment.
• Develop frameworks for model validation, monitoring, and lifecycle management.
• Research and implement new machine learning techniques that can improve predictive performance and operational robustness.
• Optimize training and inference workloads for speed and scalability.

Research Infrastructure Engineering

• Develop and maintain scalable research infrastructure supporting quantitative research activities.
• Improve distributed computing frameworks used for large-scale experimentation and model training.
• Design tools that facilitate efficient data processing, feature generation, parameter optimization, and validation.
• Build systems that allow researchers to move from concept generation to production deployment efficiently.
• Enhance reproducibility, reliability, and transparency throughout the research process.

Data Engineering & Compute Optimization

• Build high-performance workflows capable of processing large volumes of structured and unstructured market data.
• Optimize data pipelines for throughput, latency, scalability, and reliability.
• Work with distributed computing technologies to support computationally intensive workloads.
• Develop solutions that maximize utilization of available compute resources.
• Implement performance improvements using advanced Python techniques and lower-level optimizations when required.

Production Systems & Deployment

• Build production-grade systems for model deployment and signal generation.
• Create monitoring tools to evaluate model performance in live environments.
• Ensure robustness, reliability, and operational stability of deployed models.
• Support the ongoing maintenance and enhancement of research and trading infrastructure.
• Partner with broader technology teams to leverage shared platforms and services across the organization.

Collaboration with Investment Teams

• Work directly alongside quantitative researchers and investment professionals.
• Translate research requirements into scalable engineering solutions.
• Contribute ideas that improve research efficiency and model development capabilities.
• Help bridge the gap between academic research and real-world implementation.
• Participate in technical and investment-focused discussions surrounding model development and deployment.

Required Qualifications

Candidates should possess:

• Master's degree, PhD, or Postdoctoral experience in Computer Science, Mathematics, Statistics, Physics, Engineering, Machine Learning, Artificial Intelligence, or another highly quantitative discipline.
• Strong academic record from a leading university or research institution.
• Minimum three years of relevant industry, research, or engineering experience.
• Demonstrated expertise in software engineering, machine learning engineering, quantitative development, or a related technical field.
• Strong analytical and problem-solving abilities.
• Excellent communication and collaboration skills.
• Ability to work effectively in a fast-paced and intellectually demanding environment.

Technical Requirements

Programming

Candidates should demonstrate strong programming expertise, including:

• Advanced Python development experience.
• Experience building scalable and maintainable software systems.
• Knowledge of performance optimization techniques.
• Familiarity with concurrent, parallel, and distributed computing architectures.
• Experience integrating Python with lower-level languages when performance requirements demand it.
• Exposure to C++ is highly desirable.

Machine Learning

Experience should include:

• Training machine learning models on complex datasets.
• Classical machine learning methodologies.
• Deep learning architectures and workflows.
• Model evaluation and validation techniques.
• Feature engineering and dataset construction.
• Hyperparameter optimization.
• Production deployment of machine learning models.
• Experiment tracking and reproducibility practices.

Infrastructure & Distributed Systems

Ideal candidates will have experience with:

• Distributed computing environments.
• Scalable data processing systems.
• Parallel computation frameworks.
• Large-scale model training workflows.
• Research platform development.
• Workflow orchestration and automation.
• Resource optimization and compute management.

Linux & Software Engineering

• Strong Linux development experience.
• Software architecture and system design.
• Testing, debugging, and performance tuning.
• Version control and collaborative software development.
• Production support and operational best practices.

Preferred Experience

Particularly attractive candidates may possess:

• Experience working within quantitative finance, systematic trading, asset management, hedge funds, or proprietary trading environments.
• Knowledge of market microstructure.
• Understanding of systematic trading strategies.
• Familiarity with quantitative research methodologies.
• Experience with backtesting frameworks.
• Understanding of research pitfalls such as overfitting, look-ahead bias, and survivorship bias.
• Exposure to production research environments supporting investment teams.
• Experience developing agentic AI systems, including tool-use frameworks, orchestration systems, and evaluation pipelines.