Join the founding team
at RFQ

We are looking for a Quantitative Trader to lead the research, development, and deployment of systematic trading strategies across equity derivatives, index futures and options, and factor-based equity portfolios.

You will work at the intersection of quantitative research, portfolio construction, execution systems, and data infrastructure — building scalable low- to mid-frequency strategies that go into live production.

• Strong foundation in multi-factor models, alpha research, and portfolio construction
• Deep understanding of options pricing, derivatives, volatility surfaces, and Greeks
• Advanced knowledge of probability, statistics, econometrics, linear algebra, and time series analysis
• High proficiency in Python (NumPy, Pandas); C++ or Java preferred
• Experience with quantitative research, hypothesis-driven strategy development, and large datasets
• Familiarity with financial data platforms (Bloomberg, Refinitiv, Quandl) is a plus
• Exposure to machine learning techniques (XGBoost, TensorFlow, PyTorch) is a plus

• Advanced degree in Mathematics, Physics, Statistics, Computer Science, or Engineering
• Prior work in systematic trading, equity derivatives, statistical arbitrage, or factor investing
• Experience deploying live strategies and working with trading infrastructure
• Exposure to cloud computing environments (AWS preferred)

As one of RFQ's founding Algo Software Engineers, you will design and own the core trading infrastructure that our strategies run on. This means building production systems on AWS, managing data pipelines in PostgreSQL, and ensuring the platform is fast, reliable, and scalable.

You are an engineer who thrives at the edge of systems and finance — someone who cares deeply about performance, correctness, and uptime.

• Design, build, and maintain RFQ's core trading infrastructure on AWS
• Own production systems — deployment, monitoring, incident response, and reliability
• Build and manage high-throughput data pipelines with PostgreSQL as the primary data store
• Develop low-latency execution systems and order management interfaces
• Work closely with quant traders and algo developers to bring research into production
• Build internal tooling, dashboards, and risk monitoring systems
• Continuously improve system performance, latency, and operational stability

• Strong software engineering background with production experience at scale
• Deep expertise in AWS — EC2, RDS, Lambda, S3, CloudWatch, and related services
• Strong PostgreSQL skills — schema design, query optimization, and data integrity
• Proficiency in Python and/or C++; experience with low-latency systems is a strong plus
• Experience with CI/CD pipelines, infrastructure as code (Terraform, CDK), and DevOps practices
• Ability to work in a fast-moving environment where decisions matter and ownership is real

• Degree in Computer Science, Engineering, or equivalent practical experience
• Prior experience in fintech, trading infrastructure, or high-performance backend systems
• Familiarity with financial market data and exchange connectivity is a strong plus

As a founding Algo Developer at RFQ, you will own the code that turns research into live, deployable trading strategies. You sit at the intersection of quantitative research and engineering — translating mathematical models into robust, backtested, production-ready algorithms.

This is a deeply technical role for someone who can write clean, fast code and also understand what the numbers mean.

• Implement and maintain quantitative trading strategies in Python, taking them from research to production
• Build, maintain, and improve RFQ's backtesting frameworks and simulation environments
• Work with large financial datasets stored in PostgreSQL on AWS — cleaning, transforming, and preparing data for research and live trading
• Collaborate with quantitative traders to implement signals, portfolio logic, and execution rules
• Develop automated testing and validation pipelines to ensure strategy integrity
• Optimize strategy code for speed, reliability, and scalability in production
• Build tools and libraries that improve the research and development workflow

• Strong Python development skills — clean, well-tested, production-quality code
• Experience with quantitative finance concepts — factors, signals, backtesting, risk models
• Solid understanding of data manipulation with Pandas, NumPy, and SQL/PostgreSQL
• Familiarity with AWS services used in data and compute workflows
• Exposure to backtesting frameworks (Backtrader, QuantConnect, or proprietary equivalents)
• Ability to read and implement academic research papers into working code
• Interest in or experience with machine learning applied to financial data is a plus

• Degree in Computer Science, Mathematics, Engineering, or a quantitative discipline
• Prior experience in algorithmic trading, fintech, or a quantitative research environment
• Active GitHub profile demonstrating real projects, tools, or research implementations