Lead Python Engineer – AI Technology

Stack

Responsibilities

• ▹Manage technical objectives and provide technical leadership for the team
• ▹Stay hands-on with day-to-day development
• ▹Work closely across teams within WorldQuant's business-facing technology organization
• ▹Mentor junior team members
• ▹Conduct code reviews
• ▹Drive architectural decisions

Requirements

• ▹Master's degree or higher in Computer Science, Engineering, or a related technical field from a top-tier institution
• ▹7+ years of experience as a Python developer, with a strong focus on data engineering and AI/ML systems
• ▹Expert-level knowledge of Python and its ecosystem, including data processing libraries like Pandas, NumPy, and PySpark
• ▹Proficiency in designing and implementing scalable, maintainable, and efficient data pipelines
• ▹Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes)
• ▹Expertise in version control systems (Git), CI/CD practices, and agile methodologies
• ▹Strong communication skills, able to explain complex technical concepts to both technical and non-technical stakeholders
• ▹Proven track record of mentoring junior engineers and fostering a collaborative team environment

Nice to have

• ▹Experience in the finance industry is a plus but not required
• ▹Experience with AI/ML frameworks such as PyTorch or scikit-learn, LLMs, agents, or systems of agents

Soft skills

What we offer

• ▹Competitive compensation package
• ▹Premium private health insurance and life insurance with a savings plan
• ▹Employee Assistance Program and fully covered sick leave
• ▹Strong culture of learning and development: training courses, library, guest speakers, share-and-learn events, global conferences
• ▹Regular offsite team buildings, annual conferences, and occasional global summits — opportunities to travel and connect with local and global teams

About the company

WorldQuant develops and deploys systematic, quantitative financial strategies across a broad range of asset classes and global markets. Through its proprietary research platform, the firm produces predictive signals (alphas) that form the foundation of a balanced, global investment platform. Its culture combines academic rigor with accountability for results, encouraging open thinking and continuous improvement.