Quantitative Development - DevOps Engineer

About Millennium

Established in 1989, Millennium is a globally diverse alternative investment company driven by innovation, evolution, and focus on delivering client results. Our culture combines individual autonomy with collaborative global support, providing an environment for continuous growth and expertise development that enables our talent to generate sustainable impact.

Team Overview

The Central Liquidity Strategies within Execution Services oversees various portfolios and products aimed at enhancing the firm's trading and execution via internal liquidity solutions for portfolio managers on risk and agency levels. This team bridges research, engineering, and operational infrastructure to support scalable workflows and dependable application deployment.

Key Responsibilities

• Architect and deploy infrastructure that accelerates research and development productivity within the CLS environment.
• Provide guidance on, establish, and uphold best engineering and infrastructure practices to ensure scalable and reliable research and production workflows.
• Assess cutting-edge tools and methodologies, judiciously selecting solutions tailored to CLS research and development requirements.
• Construct robust, reproducible polyglot environments incorporating Python, native extensions, and CUDA for fast iterations in research and seamless production rollouts.
• Enhance packaging standards and development protocols for extensive research codebases.
• Collaborate with cloud infrastructure teams to scale research operations and boost execution efficiency.
• Develop continuous integration and delivery pipelines, monitoring systems, and automation frameworks for research workflows and live trading applications.
• Design tools that enable researchers and developers to profile, monitor, and gain insights into the performance of applications and infrastructure.

Required Qualifications & Experience

• Minimum of 7 years in DevOps environments focused on research sectors such as HPC, machine learning research, or quantitative research, coupled with experience managing high-availability production deployments.
• Excellent communication aptitude and proven ability to collaborate with diverse stakeholders within team settings.
• Demonstrated leadership capabilities including effective decision-making under pressure, working within constraints, and accountability for results.
• Proficient coding skills with a focus on writing clean, maintainable, and testable code to automate infrastructure management and deployment.
• Strong understanding of operating systems, particularly Linux internals, along with experience in software deployment and package management.
• In-depth knowledge of Python internals and contemporary Python packaging standards.
• Experience with modern packaging and build tools such as uv and hatchling.
• Familiarity with technologies including Nix, Conda, Pixi, Kubernetes, and containerization.