Senior Quant Research Engineer, Trading & Portfolio Optimization

Arta Finance•Mountain View, CA

1d•$110,000 - $300,000•Hybrid

About The Position

Arta is seeking a Senior Quant Research Engineer to join their team. This role is responsible for designing the models that determine portfolio allocations and the logic for executing trades efficiently. The ideal candidate will combine quantitative finance expertise with strong software engineering skills to build and deploy production systems for managing client portfolios at scale. This is a hybrid role where you will work closely with the investment team and engineering to ensure strategies are robust and run reliably.

Requirements

5 years of experience or strong interest from working close to markets or portfolios (quant researcher, trader, or PM-facing capacity).
Strong quantitative finance background with fluency in portfolio theory, optimization, and risk.
Rigorous math foundation: linear algebra, optimization, probability, and statistics.
Strong software engineering skills with the ability to take a model from research to a production-quality system.
Comfortable working across the full stack of a quantitative system: data, models, and services.
Understanding of tax-aware investing concepts (e.g., tax-loss harvesting).
Fluency with AI coding tools and a track record of using them effectively.
Excellent communication skills, with the ability to work directly with investment leadership.
Ability to thrive in a fast-paced startup environment with strong problem-solving skills, high ownership, and comfort working independently amid ambiguity.

Responsibilities

Design, build, and improve models for target portfolio allocations, balancing risk, return, and client-specific constraints.
Build and maintain systems for translating target allocations into real trades, focusing on tax efficiency and cost-aware execution.
Apply risk and portfolio management techniques (optimization, factor-based risk modeling, statistical estimation) to maintain strategy robustness.
Backtest and validate new models and trading logic against historical data.
Contribute an investment/portfolio-management perspective to team tradeoffs.
Partner with investment, product, and engineering teams to develop production-quality systems.
Utilize AI coding tools to enhance research and development speed and quality.