Quantitative Software Engineer
New York City / San Francisco / Los Angeles / Remote /
Gauntlet is DeFi’s risk manager. We drive capital efficiency while maintaining economic safety for some of the largest crypto protocols with our simulations. Gauntlet manages risk and incentives for over $40 Billion in assets.
Gauntlet continuously publishes cutting-edge research, making us the most cited peer-reviewed articles in the DeFi industry. We’re a Series B company with ~60 employees operating remote-first with a home base in New York City.
Gauntlet’s mission is to help make blockchain protocols and smart contracts safer and more trustworthy for users. Building decentralized systems creates new challenges for protocol developers, smart contract developers, and asset holders that are not seen in traditional development and investing.
We are seeking a highly skilled and motivated Quantitative Software Engineer to join our team. The ideal candidate is one who possesses strong statistical and engineering skills, has a passion for problem-solving, and be able to work effectively in a fast-paced and collaborative environment.
- Conduct quantitative research, utilizing a broad range of methods and sophisticated data analytics tools to develop statistical models and improve strategies.
- Work closely with cross-functional teams to understand their data requirements, ensure the data integrity, and offer analytical support.
- Develop predictive models and machine learning algorithms, interpreting the outcomes and providing actionable insights to the business stakeholders
- Present research findings and data insights to non-technical stakeholders in a clear, concise, and accessible way.
- Maintain up-to-date knowledge of the latest industry trends, technologies, and techniques in quantitative research and data analysis
- Assist in designing and implementing new data models and algorithms, improving the overall effectiveness of our quantitative research efforts.
- 4+ years of professional engineering experience in a quantitative role, preferably within the financial services and quant trading.
- Proficient at writing code in Python or similar interpreted languages
- Excellent understanding of statistical modeling, machine learning, and optimization algorithmsExperience 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
- Experience working in the crypto industry is a plus but not required.
- Master’s or Ph.D. in Quantitative fields like Mathematics, Economics, Computer Science, Physics, or similar fields is a plus
- Published or presented research in the space
Benefits and Perks
- Remote first - work from anywhere in the US & CAN!
- Regular in-person company retreats and cross-country "office visit" perk
- 100% paid medical, dental and vision premiums for employees
- Laptop, monitor, keyboard and mouse setup provided
- $1,000 WFH stipend
- Monthly reimbursement for home internet, phone, and cellular data
- Unlimited vacation
- 100% paid parental leave of 12 weeks
- Fertility benefits
- Opportunity for incentive compensation
Please note at this time our hiring is reserved for potential employees who are able to work within the contiguous United States and Canada. Should you need alternative accommodations, please note that in your application.
The national pay range for this role is $150,000 - $180,000 base plus additional On Target Earnings potential by level and equity in the company. Our salary ranges are based on paying competitively for a company of our size and industry, and are one part of many compensation, benefits and other reward opportunities we provide. Individual pay rate decisions are based on a number of factors, including qualifications for the role, experience level, skill set, and balancing internal equity relative to peers at the company.