Senior Research Engineer (Machine Learning)

TL;DR

Senior Research Engineer (Machine Learning): Helping lead the development of a trading model research framework and using it to conduct research that develops models for trading in production with an accent on end-to-end ML pipelines, model training/backtesting, and integration into production deployment. Focus on global rollout of the framework and partnering with Quantitative Researchers to tune data-driven systematic trading strategies and alpha signals.

Location: Must be based in Hong Kong (relocation to the Hong Kong office)

Company

hirify.global is a proprietary trading firm building data-driven trading models.

What you will do

• Lead the development and global rollout of a research framework for defining, training, and optimizing trading models (e.g., supervised learning, backtesting).
• Integrate the framework with the platform to deploy and run models in production.
• Partner with Quantitative Researchers to test hypotheses and tune/develop data-driven systematic trading strategies and alpha signals.

Requirements

• Advanced degree (Master’s or PhD) in Machine Learning, Statistics, Physics, Computer Science, or similar.
• 8+ years of hands-on experience in MLOps, Research Engineering, or ML Research.
• Strong background in mathematics and statistics.
• Strong Python proficiency and experience with libraries such as numpy, pytorch, polars, pandas, and ray.
• Experience designing and implementing end-to-end ML pipelines: data preprocessing, model training, deployment, and monitoring.
• Experience with modern software development practices and tools (Agile, version control, automated testing, CI/CD, observability) and cloud/container technologies (AWS/Azure/GCP, Docker, Kubernetes).

Culture & Benefits

• Entrepreneurial and innovative culture in a proprietary trading environment.
• Opportunity to help build a global standard for training and consuming data-driven trading models.
• Relocation to the Hong Kong office for the successful candidate.

Hiring process

• Interviews and evaluation of ML research and engineering experience.
• Discussion of fit for building and deploying trading model research frameworks.