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Overview
Mid-Level

Finance Expert - Quantitative Trading

Confirmed live in the last 24 hours

xAI

xAI

Remote
Remote
Posted April 1, 2026

Job Description

About xAI

xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates.

ABOUT THE ROLE:

As a Quantitative Trader, you will play a key role in improving xAI's advanced AI systems by delivering high-quality annotations, evaluations, and expert input using specialized labeling tools. You will collaborate closely with our technical teams to support the development and refinement of new AI capabilities, with a particular emphasis on quantitative trading domains. Your expertise will help select and solve challenging problems in systematic and quantitative strategies — including statistical arbitrage, factor investing, market microstructure modeling, high-frequency / execution algorithms, risk premia harvesting, machine learning-based alpha generation, and portfolio optimization under realistic constraints. This role requires strong analytical thinking, rapid adaptation to evolving guidelines, and the ability to provide rigorous, technically sound critiques and solutions in a fast-moving environment.

As a Quantitative Trader, you will directly contribute to xAI's mission by helping train and refine our frontier AI models. You will teach the models how quantitative traders reason, model markets, evaluate signals, manage risk, and interact with complex financial data and systems. This involves providing high-quality data in various formats (text, voice, video), writing detailed annotations, critiquing model outputs, recording audio explanations, and occasionally participating in structured video sessions. We are looking for individuals who are enthusiastic about these data-generation activities, as they form a core part of advancing xAI’s goals in scientific discovery and real-world reasoning.

Quantitative Traders provide labeling, annotation, evaluation, and expert reasoning services across text, voice, and video data modalities to support model training and evaluation. The role may include recording audio responses, participating in video-based tasks, or producing step-by-step quantitative reasoning traces — all of which are essential job functions required to fulfill xAI’s mission. All outputs are considered work-for-hire and owned by xAI.

RESPONSIBILITIES:

  • Use proprietary annotation and evaluation software to provide precise labels, rankings, critiques, and detailed solutions on assigned projects
  • Deliver consistently high-quality, curated data that meets strict technical and scientific standards
  • Collaborate with engineers and researchers to support the creation and iteration of new training tasks and evaluation benchmarks
  • Provide feedback that helps improve the usability, efficiency, and precision of annotation and data-collection tools
  • Select and solve complex problems from quantitative trading domains where you have deep expertise — examples include:
    • Factor model construction and signal combination
    • Market microstructure and order book dynamics
    • Statistical arbitrage and pairs/cointegration strategies
    • ML-driven alpha generation and feature engineering
    • Optimal execution algorithms and transaction cost modeling
    • Portfolio construction under constraints (risk, turnover, sector, etc.)
    • Risk modeling and stress-testing frameworks
  • Deliver rigorous model critiques, alternative solutions, mathematical derivations, and quantitative reasoning when evaluating AI outputs
  • Interpret,
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