Remote Quantitative Developer — Fintech Trading & Analytics

Bright Vision Technologies; Beaverton (OR); Remote; USD 75,000 - 100,000; Full time

Job summary

Bright Vision Technologies is seeking an experienced Quantitative Developer to build low-latency trading, risk, and analytics systems for fintech applications. You will partner with quants and traders to translate models into production-quality software that meets strict performance and reliability standards.

The ideal candidate has strong engineering discipline, deep quantitative fundamentals, and hands-on experience with market data, FIX, and high-throughput systems.

Qualifications

• Bachelor's or Master's degree in Computer Science, Mathematics, Physics, or a related quantitative discipline.
• Six or more years of software engineering experience, with significant time in fintech.
• Strong programming skills in C++, Java, or Python (preferably more than one).
• Solid grounding in financial markets, instruments, and basic quantitative methods.
• Hands-on experience building low-latency, high-throughput systems.
• Experience with market data systems and FIX protocol implementations.
• Strong understanding of risk and P&L attribution.
• Experience with high-performance computing patterns and concurrency.
• Excellent debugging, profiling, and performance-tuning skills.
• Strong communication and documentation skills.

Skills

C++/Java/Python

Low-latency systems

Software engineering

Financial markets knowledge

FIX protocol

Performance tuning

Concurrency

Documentation

Communication

Education

Bachelor’s or Master’s degree in Computer Science, Mathematics, Physics, or a related quantitative discipline

Tools

QuantLib

kdb+/q

Job description

Bright Vision Technologies is seeking an experienced Quantitative Developer to build low-latency trading, risk, and analytics systems for fintech applications. You will partner with quants and traders to translate models into production-quality software that meets strict performance and reliability standards.

The ideal candidate has strong engineering discipline, deep quantitative fundamentals, and hands-on experience with market data, FIX, and high-throughput systems.