Quantitative Developer (Fintech)

Bright Vision Technologies•Fremont, CA

1d•Remote

About The Position

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled Quantitative Developer (Fintech) to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. We are seeking an experienced Quantitative Developer to build low-latency, high-reliability trading, risk, and analytics systems for fintech applications. In this role you will partner closely with quants and traders to translate mathematical models into production-quality software that meets strict performance, accuracy, and operational requirements. The ideal candidate will combine strong software engineering skills with solid quantitative fundamentals and deep familiarity with financial markets, instruments, and risk management practices. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production.

Requirements

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.

Nice To Haves

Experience with derivatives pricing libraries (QuantLib).
Familiarity with kdb+/q or similar columnar tick databases.
Exposure to GPU-accelerated pricing or risk computation.
Experience with cloud-native fintech architectures.
Advanced degree in a quantitative discipline.

Responsibilities

Design and implement low-latency trading, pricing, and risk systems in C++, Java, or Python.
Translate quantitative models from prototypes (often in Python or MATLAB) into production-quality implementations.
Build robust market data ingestion and normalization pipelines for high-volume tick data.
Develop pricing libraries for derivatives and structured products, with rigorous testing against analytical benchmarks.
Implement risk engines, P&L attribution systems, scenario analysis tools, and stress-testing capabilities used by traders, risk managers, and quants to make informed decisions under uncertain market conditions.
Profile and optimize critical-path code for latency and throughput, applying systematic measurement, targeted improvements, and data-driven validation to deliver quantifiable gains in throughput, latency, or resource efficiency.
Build comprehensive backtesting and simulation infrastructure that lets researchers evaluate strategies against historical data and synthetic scenarios with reproducible, audit-friendly results.
Collaborate closely with quants, traders, and risk officers to refine models and tooling.
Implement regulatory and compliance reporting workflows where applicable, ensuring outputs meet jurisdictional requirements, are auditable end-to-end, and can be reproduced reliably for retrospective analysis.
Ensure full observability of trading systems with appropriate logging, metrics, and audit trails.
Lead incident response for trading-critical issues with calm and rigor.
Maintain comprehensive, current technical documentation — including architecture diagrams, design decisions, configuration references, runbooks, and operational procedures — so that the system remains supportable, auditable, and easy to onboard new engineers onto over time.
Mentor junior engineers and contribute to engineering culture in the team.