Quant Research Engineer

Qualifications & Experience

• 3–5 years of professional experience in a quantitative development role, focused on building and maintaining quantitative research and production pipelines. Alternatively, significant engineering experience in a fast-paced startup — or strong hands-on AI/LLM engineering experience (building production LLM applications, agentic systems, or AI-powered developer

tooling) — with demonstrated ownership of complex infrastructure will be considered in lieu of direct quant experience.

• Proven, end-to-end ownership of a significant piece of trading, research, high-performance, or AI infrastructure.
• Deep expertise in modern C++ and Python in a high-performance computing context.
• Demonstrable experience with large-scale data infrastructure (e.g., real-time/streaming and historical tick data).
• Strong background in cloud computing (AWS, GCP, or Azure) and parallel computing paradigms.

Hard Skills & Technical Knowledge

• Broad knowledge of the technology landscape and the judgment to select the right tool for the problem (e.g., KDB+, Apache Spark, Dask, Redis).
• Practical experience applying LLMs and agentic workflows to real engineering or research problems — LLM APIs, agent frameworks, retrieval-augmented generation, and structured output pipelines — with sound judgment about where AI adds value and where determinism must be preserved.
• Proficiency with different database designs — SQL, NoSQL, and distributed file systems.
• Experience with containerization and orchestration technologies (Docker, Kubernetes).
• Strong experience with DevOps practices: infrastructure-as-code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, GitLab CI), and system observability — including familiarity with AI-assisted operations tooling.

Soft Skills

• Exceptional Logical & Reflective Thinking: Ability to deconstruct complex problems and design elegant, effective solutions.
• Proactive Collaboration: A team player who thrives in a collaborative environment and builds strong partnerships.
• High Integrity: Takes initiative and ownership of projects, upholding rigorous ethical standards in handling sensitive data and models.
• Growth Mindset: Innate curiosity and commitment to continuous improvement — including genuine enthusiasm for the rapidly evolving AI landscape and a track record of adopting new tools ahead of the curve.
• Superb Communication: Can articulate complex technical concepts to both technical and non-technical stakeholders.

Read the job posting on the Millennium site

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