Algo Trading Developer Stamford, CT

Join Us in Shaping the Future of Decentralized Intelligence

At Yuma, we are on a mission to drive positive economic and societal change by revolutionizing the way the world connects through decentralized intelligence. Yuma champions development on Bittensor, an ecosystem that democratizes access to advanced computing and intelligence. We support transformative teams at the early stages of development, providing tools and resources to help them succeed and to ensure the AI revolution is accessible to the next generation of visionaries.

As an Algo Trading Developer at Yuma, you will participate in building and executing on-chain trading strategies within the Bittensor ecosystem. This role covers quantitative strategies trading across alpha research, data processing, backtesting, portfolio construction, and performance monitoring in a collaborative team. You will work with the Investment Lead and Engineering teams to research, implement, and refine strategies that apply quantitative methods to decentralized, on-chain markets.

• Develop and actively monitor dynamic TAO trading strategies across a portfolio of subnet tokens
• Research on-chain market inefficiencies and arbitrage opportunities to generate short-term alpha
• Design and implement proprietary trading models using statistical and machine learning techniques

• Analyze liquidity, slippage, and order book depth to optimize trade execution across decentralized venues
• Monitor real-time market conditions and adjust execution logic to minimize cost and maximize fill quality
• Stay current on MEV dynamics, protocol upgrades, and emerging tooling that impact execution efficiency

• Build and maintain robust backtesting frameworks and performance monitoring dashboards
• Develop infrastructure to support block-by-block execution of trading strategies
• Collaborate with engineering to design scalable data pipelines and optimize system architecture
• Contribute to the development of internal tooling for strategy deployment, monitoring, and debugging

• Brilliant analytical and problem-solving skills
• Solid understanding of statistics

• Bachelor's or Master’s degree in Math, CS, Stats, Physics, or related field
• 4+ years of experience in quantitative development within trading or digital asset environments
• Proven interest in financial markets and trading strategies
• Creative, self-driven, and highly detail-oriented
• Strong communicator and collaborative team player

• Prior experience developing algorithmic trading models for digital asset hedge funds or quant desks
• Proficiency in Python, Rust, and SQL
• Experience with on-chain data analysis
• Familiarity with DeFi protocols
• Familiarity with the Bittensor ecosystem, including TAO and Subnet Tokens

• An opportunity to thrive in a dynamic, cutting-edge environment at a rapidly scaling company led by experienced industry leaders
• An innovative learning environment with opportunities to contribute to building a transformative new industry
• Competitive base salary, bonus and incentive compensation
• Unlimited PTO / Flexible time off
• Professional development budget for personal and professional growth
• Outstanding health insurance for employee, partner and dependents
• Life insurance, short-term & long-term disability coverage
• 401K plan with company contribution
• Flexible spending programs for medical and dependent care
• Paid parental leave

At Yuma, our mission is to create positive economic and societal change by connecting transformative ideas to decentralized intelligence. We support and encourage development on Bittensor – an ecosystem that rewards our brightest minds and opens access to advanced computing and intelligence. We are an Equal Opportunity Employer and embrace diversity.

Yuma is a subsidiary of DCG, a global investor, builder and incubator.

Yuma does not discriminate on the basis of race, color, religion, gender (including pregnancy or related medical conditions), gender identity, sexual orientation, national origin, age, disability, or any other non-merit-based factors.

Disclaimer:
The above statements describe the general nature and level of work performed by employees assigned to this classification and are not intended to be exhaustive.