Machine Learning Lead Algo Trading

This is a remote position.

Job Title: Machine Learning Lead

We are seeking a Machine Learning Lead to spearhead our quantitative research initiatives in machine learning-driven trading. This role is ideal for a hands-on leader with deep expertise in machine learning infrastructure, predictive modeling, and trading strategies. 

We fund and nurture ML-based quant trading teams, offering capital allocation for research and strategy development. You will have full autonomy to define your research direction, team structure, and execution approach without rigid constraints. We prioritize teamwork over internal competition, ensuring that each team operates independently while sharing insights and resources. With access to cutting-edge technology, data, and execution platforms, you will have the opportunity to develop and test your models at scale.

Key Responsibilities

• Build and lead a small team of quantitative researchers and engineers dedicated to machine learning applications in trading.
  

• Develop and optimize machine learning pipelines, ensuring scalability, efficiency, and adaptability to market conditions.
  

• Architect a feature library and predictive model suite to enhance trading strategies across multiple asset classes.
  

• Execute research ideas by translating ML insights into profitable, risk-managed trading strategies.
  

• Conduct rigorous backtesting and validation to ensure robustness and alignment with trading objectives.
  

• Collaborate closely with quant research groups and trading teams to integrate predictive models into execution frameworks.
  

• Continuously refine models based on real-time performance, adapting to evolving market conditions.
  

Requirements

• Proven leadership experience in managing teams in machine learning quantitative research, preferably within hedge funds or technology firms.
  

• Strong technical expertise in building and optimizing machine learning infrastructure
  

• Advanced technical skills in statistics, mathematics, and programming, with a particular focus on Python. Proficiency in additional languages like C++ or Rust is a plus.
  

• Expertise in feature engineering, hyperparameter optimization, and monetization of forecasts. 
  

• Experience in quantitative trading, particularly in MFT (Medium-Frequency Trading), with a demonstrable PnL track record.
  

• Ability to specialize in specific asset classes and markets, including ICE/CME through tBricks, India (options), China (futures), and Brazil. 
  

• Advanced degree (PhD or Master’s) in a quantitative discipline 
  

Preferred Qualifications

• Experience specifying technical infrastructure needs, including core memory, latency, and GPU configurations. 
  

• Hands-on experience in specifying and managing technical infrastructure for large-scale ML simulations.
  

• Proficiency in developing feature factory libraries and conducting large-scale signal testing.
  

• Track record of building profitable trading models with real-world implementation.
  

• Strong communication skills to bridge the gap between research and trading teams.
  

• Innovative Work Environment: Be part of a state-of-the-art, global tech team that cherishes innovation and is devoid of bureaucratic hindrances.
  
• Career Growth: Enjoy unparalleled opportunities for professional and personal growth, access to industry-leading mentors, and invitations to professional seminars globally.
  
• Flexibility: Relish the freedom to work remotely from any corner of the globe, ensuring a harmonious work-life balance.
  
• Competitive Compensation: Receive a highly competitive fixed income, reflecting your expertise and contribution to the team.
  
• Performance-Based Bonuses: Enjoy bonuses determined by systematic evaluations, ensuring that your hard work and contributions are always recognized.
  
• Profit Sharing: Align your success with the company's growth, benefiting directly from the profits the team collectively generates.
  
• Long-term Incentives: Be part of a system that values longevity and dedication, offering long-term stimulation mechanisms to reward consistent performers.