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About Us DORA is building the next generation of bond markets through a cutting-edge API-driven protocol. By integrating sophisticated mathematical models and novel algorithms for debt markets, DORA enables 24/7 liquidity, full transparency, and sub-millisecond transaction latency for bond trading. The protocol is fully compatible with tokenized assets (RWA's), next-gen stablecoins, and all forms of fixed income.

Our partners include HFT quant firms, Algo traders, macro funds, institutional trading desks, and global asset managers. DORA is backed by some of the largest digital asset firms, Wall Street banks, and hedge funds. About the Role We are seeking a highly skilled and entrepreneurial quantitative trading technologist to lead strategic partnerships and drive product integrations across our trading stack.

This role sits at the intersection of quant research, algo trading systems, infrastructure integration, and business development. You will work closely with external partners (exchanges, brokers, data providers), and internal stakeholders (quant researchers, traders, engineers) to evaluate, negotiate, and implement solutions that directly impact our trading and revenue outcomes. Key Responsibilities Identify, assess, and secure partnerships with exchanges, liquidity providers, and technology vendors.

Own the integration lifecycle of trading infrastructure (market data, execution, analytics, risk). Collaborate with quant researchers to align integration priorities with trading strategy goals. Design and manage technical POCs for connectivity, data ingestion, and algo execution.

Contribute to technical architecture decisions—especially around latency, throughput, and system reliability. Develop documentation and internal tooling to streamline partner onboarding and testing. Represent the firm in technical and commercial discussions with strategic partners.

Requirements Technical/Quant Skills: Strong programming skills in C++, Rust, Go, or Python. Solid foundation in algorithms, data structures, and systems programming. Experience with low-latency trading systems, market data ingestion, and execution engines.

Deep understanding of quantitative trading models, signal research, and strategy deployment. Ability to read and interpret quant codebases and engage in performance optimization. Familiarity with FIX protocol, WebSocket/REST APIs, Kafka, Redis, ClickHouse, etc.

Quantitative/Mathematical Background: Strong academic background in Mathematics, Computer Science, Physics, or Engineering. Familiarity with statistical modeling, time series analysis, and machine learning is a plus. Business/Strategic Skills: 5–10+ years in quantitative trading, fintech, or trading infrastructure roles with commercial exposure.

Demonstrated experience managing high-stakes integrations and external partnerships. Comfort navigating between product, technical, and business discussions with precision and clarity. Bonus Points Experience at a market maker, HFT firm, hedge fund, or trading-focused fintech startup.

Exposure to risk engines, real-time monitoring systems, or strategy backtesting frameworks. Existing network among key trading infrastructure vendors and institutional players. What You'll Gain: Influence over core trading infrastructure and commercial strategy.

High-impact engineering, trading, and executive leadership. Opportunity to shape our technology partnerships and contribute to competitive edge. About the Founder Brent Xu, the founder of DORA, previously built Umee, the most modular algorithmic lending protocol in Web3 that has generated $250M+ in lifetime loans as well as over $1Bn USD in cross chain transactions.

Over his startup journey, he has raised $42M+ in venture capital funding from top investors, including Polychain, Coinbase, ConsenSys, IDEO CoLab Ventures, Jump Trading, GSR, and Brian Kelly Capital Management. Before launching startups, Brent was among the first 20 employees at ConsenSys, where he played a key role in Ethereum's early development. Prior to blockchain, he was a bond trader specializing in high-yield credit, distressed debt, and mortgage-backed securities, trading $50-$100 Bn in notional value annually on Wall Street.

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