Gauntlet - Quantitative Software Engineer

Gauntlet leads the field in quantitative research and optimization of DeFi economics. We manage market risk, optimize growth, and ensure economic safety for protocols facilitating most spot trading, borrowing, and lending activity across all of DeFi, protecting and optimizing the largest protocols and networks in the industry. We build institutional-grade vaults for decentralized finance, delivering risk-adjusted onchain yields for capital at scale. Designed by the most vigilant, quantitative minds in crypto and informed by years of research. As of April 2025, Gauntlet manages risk and incentives covering over $42 billion in customer TVL. Gauntlet continually publishes cutting-edge research that informs our risk models, alerts, and analysis, and is among the most cited institution — including academic institutions — in terms of peer-reviewed papers addressing DeFi as a subject. We’re a Series B company with around 75 employees, operating remote-first with a home base in New York City. Our mission is to drive adoption and understanding in the financial systems of the future. The unique challenges of decentralized systems call for innovative approaches in mechanism design, smart contract development, and financial product utilization. Gauntlet leads in advancing this knowledge, ensuring safe progression through the evolving landscape of financial innovation. We are seeking highly skilled and motivated Quantitative Software Engineers to join our team. The ideal candidate possesses strong statistical and engineering skills, a passion for problem-solving, and the ability to work effectively in a fast-paced and collaborative environment. Responsibilities: Designing and implementing strategies for managing risk and optimizing DeFi protocols using quantitative models, simulations, and machine learning. Develop tools and engines for parameter recommendations and drive impact to protocols. Own the whole lifecycle of protocol integrations, including building data pipelines, working closely with cross-functional teams to define the data requirements and product offering. Architect and refine data models and structures to support the evolving needs of DeFi analytics, simulations, methodologies and research development. Contribute to making our core modeling platform world-class. Maintain up-to-date knowledge of the latest industry trends, technologies, and techniques in software engineering. Optimize Aera guardian logic to improve risk-adjusted yields, trade execution quality, and capital efficiency of strategies such as Protocol-Owned Liquidity for Aera Vaults. Collaborate with other cross-functional teams, including internal and external teams Stay current with the latest industry trends, market risk vectors, and market conditions to ensure that Aera strategies stay on the cutting edge of crypto and DeFi innovation. Bonus Points: Contribute to the forefront of DeFi economic understanding and optimization. Work on projects that value deep research, quality, and practical outcomes. Collaborate with a team committed to defining future financial systems. Master’s or Ph.D. in Quantitative fields like Mathematics, Economics, Computer Science, Physics, or similar fields is a plus. Benefits and Perks Remote first - work from anywhere in the US & CAN! Competitive packages with the added opportunity for incentive-based compensation Regular in-person company retreats and cross-country "office visit" perk 100% paid medical, dental and vision premiums for employees Laptop provided $1,000 WFH stipend upon joining $100 per month reimbursement for fitness-related expenses Monthly reimbursement for home internet, phone, and cellular data Unlimited vacation policy 100% paid parental leave of 12 weeks Fertility benefits Qualifications Minimum 4 years of direct hands-on experience trading or analyzing financial markets (crypto or traditional) professionally. Experience developing statistical or quantitative models for financial markets. Understanding of blockchain and DeFi protocols, concepts, and best practices (or a strong desire to learn). Proficient at writing code in Python and SQL with a solid understanding of software engineering principles. Knowledge of workflow orchestration (e.g., Dagster, Airflow) and distributed data processing technologies (Spark). Excellent understanding of statistical modeling, machine learning, and optimization algorithms. Experience with scientific computing packages such as Numpy/Scipy, Pandas, etc. Ability to quickly internalize abstract concepts in new domains, coupled with strong problem-solving skills and attention to detail. Ability to work independently and within a team, manage multiple projects, and meet deadlines. Strong communication skills and the ability to work collaboratively in a distributed team environment.