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AXQ Capital

Posted 3 months ago

Open

AI Engineer

Beijing, ShanghaiRemoteFull-time

AI Summary

AI Engineer designs and builds agentic AI research infrastructure for quantitative finance, collaborating with the research team to push frontier AI with real market feedback.

About this role

About Us

AXQ Capital is a global quantitative investment firm with offices in New York, Beijing, Shanghai, and Hong Kong. We pursue consistent alpha through rigorous scientific research and sustained investment in technology and data infrastructure. Our strategies are deployed across global markets, spanning multiple geographies, asset classes, and trading horizons.

Job Duties

As an AI Engineer, you will build the next generation of agentic AI research infrastructure. Working closely with our quantitative research team, you will push the frontiers of modern AI — with the rare advantage of real financial market feedback as your ground truth.

  • Design and build a multi-agent framework for quantitative finance research
  • Develop tools and deep integrations with AXQ's proprietary research infrastructure
  • Research frontier model capabilities and develop rigorous agentic evaluations
  • Partner with quantitative researchers to create best-in-class system observability, well-grounded research pipelines, and novel research ideation methods
  • High-performing candidates may also contribute directly to model work, including post-training

Qualifications

  • 2+ years of experience in software engineering, machine learning, AI research, or a related role
  • Bachelor's, Master's, or PhD from a top-tier university in a quantitative or technical discipline (mathematics, physics, statistics, computer science, or similar)
  • Strong foundation in statistics and machine learning fundamentals
  • Deep, practical knowledge of agentic systems, pre/post-training evaluations, or related AI engineering domains
  • Prior experience in quantitative finance is not required
  • Self-motivated and highly productive, with a strong sense of ownership and a bias toward action

Skills

Agentic SystemsAI EngineeringDeep LearningIntegration With Proprietary Research InfrastructureMachine LearningModel EvaluationMulti-agent FrameworkObservabilityPost-trainingQuantitative Finance ResearchResearch PipelinesStatistical Foundations

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