Quant Research Engineer

About the Role

We are seeking a Quant Research Engineer to take ownership of our core quantitative pipelines, data infrastructure, and computational environments that support research and production activities in quantitative trading. Candidates should have a strong academic background, preferably with PhD-level training in relevant technical fields and a history of excellence on an international scale, and experience in leading quantitative firms or AI technology companies. A passion for integrating AI, especially LLM-based tools, into engineering workflows is highly valued.

Key Responsibilities

• Develop, maintain, and enhance the firm’s essential quantitative data and compute systems, ensuring high reliability and scalability.
• Lead the architectural design for a cutting-edge data and compute platform with embedded AI features such as large language model services and agentic workflows.
• Collaborate closely with Quantitative Researchers and related teams to gather requirements and seamlessly incorporate new components into the infrastructure.
• Serve as the central authority on data and computation flow across teams, building AI-powered tools to streamline research processes from data exploration to prototyping.
• Set and enforce rigorous standards for system design, coding, testing, and deployment practices.
• Manage deployment, monitoring, and overall health of production and research systems, using AI-augmented methods such as anomaly detection and intelligent incident triage.
• Promote infrastructure-as-code and automation of operational workflows, applying AI coding agents and LLM tools when they improve speed and quality.

Qualifications & Experience

• 3 to 5 years’ experience in quantitative development focusing on research and production pipelines or comparable engineering experience in high-velocity startup environments with LLM engineering ownership.
• Proven full lifecycle responsibility for substantial trading, research, high-performance, or AI infrastructure projects.
• Strong expertise in modern C++ and Python within high-performance computing contexts.
• Experience managing large-scale data infrastructure handling both streaming and historical tick data.
• Proficient in cloud platforms such as AWS, GCP, or Azure, and parallel computing paradigms.

Technical Skills

• Broad understanding of various technologies and ability to select appropriate tools such as KDB+, Apache Spark, Dask, and Redis for specific problems.
• Hands-on experience incorporating LLM APIs, agent frameworks, retrieval-augmented generation, and structured output pipelines into practical applications.
• Familiarity with diverse database systems including SQL, NoSQL, and distributed file systems.
• Experience with containerization (Docker) and orchestration (Kubernetes) technologies.
• Expertise in DevOps techniques such as infrastructure-as-code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, GitLab CI), and implementing system observability enhanced by AI operations tools.

Soft Skills

• Outstanding logical reasoning and analytical skills for solving complex technical challenges.
• Effective collaborator who builds strong, cooperative relationships within teams.
• Demonstrates responsibility, integrity, and ownership of projects, with high ethical standards handling sensitive information.
• Committed to continuous learning with a keen interest in emerging AI technologies and early adoption of innovative tools.
• Excellent communication skills capable of explaining technical concepts clearly to diverse audiences.