Senior Quantitative Developer
We are currently partnered with a financial services firm in California that is expanding its quantitative engineering team. They're hiring a Quantitative Developer / Financial Engineer to help build and evolve a research and analytics platform used by systematic investment and research teams.
This role focuses on developing scalable, production‑quality tools that support the full lifecycle of systematic strategy development, including data handling, signal research, portfolio construction, backtesting, and performance analytics. A key aspect of the role is close collaboration with senior engineering leadership and quantitative stakeholders. The person in this role will work on high‑impact technical initiatives, help shape architectural decisions, and partner closely with researchers to translate analytical workflows into reliable software systems.
This is a strong fit for someone who enjoys building robust, well‑tested infrastructure for quantitative users and working at the intersection of research and engineering.
What You'll Do
- Build and maintain a modular framework supporting systematic research workflows (signals, portfolio construction, backtesting, and analytics)
- Engineer solutions that span research experimentation and production use cases
- Design clean, well‑documented APIs and libraries used by quantitative teams
- Collaborate with engineering and product stakeholders on system architecture, quality, and delivery
- Partner with quantitative researchers to prototype, iterate, and productionize new capabilities
What You'll Bring
- 4+ years of experience as a quantitative developer or similar role building production‑quality Python systems in finance or data‑intensive environments
- Strong familiarity with systematic research and model‑driven investment workflows, including portfolio analytics and performance evaluation
- Experience working with large datasets (especially time‑series), with an emphasis on reliability, scalability, and reproducibility
- Production experience with Python's numerical computing and machine learning ecosystem
- Experience supporting research‑to‑production workflows with close researcher collaboration
- Solid software engineering fundamentals, including API design, system architecture, testing, and deployment practices
- Exposure to distributed data processing and/or cloud‑based environments
- Bachelor's, Master's, or PhD in a quantitative field such as computer science, mathematics, engineering, statistics, or financial engineering
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