Quantitative Software Engineer

The Opportunity:

• We are looking for a highly technical software engineer to help build production-grade systems for automated asset management and quantitative trading. The ideal candidate is strongest in Python, backend engineering, data systems, and machine learning infrastructure.
• Deep quantitative finance knowledge is not required. However, interest in financial markets is important, and prior experience in trading systems, market data, asset management, or HFT is highly preferred.

 
What You’ll Do:

• Build and maintain our Python/Django-based asset-management and trading platform.
• Design scalable backend services, REST APIs, data models, and internal tools.
• Develop reliable data pipelines for market data ingestion, APIs, scheduled jobs, and web-based data sources.
• Optimize SQL database performance across PostgreSQL, SQL Server, or similar relational databases.
• Convert research prototypes into clean, testable, production-ready code.
• Support ML-driven signal generation, model deployment, monitoring, and analytics workflows.
• Build dashboards and reporting tools for internal research, portfolio monitoring, and client use.
• Collaborate with quantitative researchers to translate models into maintainable software.
• Improve platform reliability, performance, observability, and scalability.
• Follow engineering practices: Git, testing, CI/CD, code reviews, documentation, and clean architecture.

 
Must-Have Qualifications:

• 2–4+ years of professional software development experience.
• Strong Python proficiency and ability to write clean, reliable, production-quality code.
• Experience with Django, FastAPI, Flask, or similar backend frameworks.
• Strong SQL skills, including database design, indexing, query optimization, and relational databases such as PostgreSQL, SQL Server, or MySQL.
• Experience working with data-heavy applications using pandas, NumPy, or similar libraries.
• Familiarity with machine learning workflows, including model training, evaluation, deployment, or monitoring.
• Strong software engineering fundamentals: Git, unit testing, debugging, design patterns, error handling, logging, and documentation.
• Strong problem-solving ability and comfort working on complex technical systems.
• Bachelor’s degree in Computer Science, Software Engineering, Data Science, Engineering, Mathematics, or a related technical field.

 
Preferred Qualifications:

• Experience in HFT, trading systems, market data platforms, execution systems, or low-latency environments.
• Exposure to asset management, quantitative finance, portfolio analytics, backtesting, or time-series data.
• Experience with data orchestration tools such as Airflow, Celery, Prefect, Dagster, or similar platforms.
• Familiarity with monitoring and observability tools such as Grafana, Prometheus, Kibana, ELK, or similar systems.
• MLOps experience, including MLflow, DVC, Docker, model versioning, deployment, and monitoring.
• Experience with cloud platforms, Linux environments, containerization, or production infrastructure.
• Master’s degree in a technical field is a plus.

 
What We Value:

• We value strong engineers who can build reliable systems, learn quickly, and take ownership. Financial markets knowledge is a plus, but technical excellence, clean code, problem-solving ability, and production mindset are the most important qualities for this role.