Senior Research Engineer - Sanderson-iKas

Sanderson-Ikas; Hong Kong; Sur place; HKD 900 000 - 1 500 000; Plein temps

Résumé du poste

Sanderson-Ikas is seeking a Senior Research Engineer in Hong Kong to design and evolve a scalable ML research framework that supports the full lifecycle from experimentation through deployment. You will build infrastructure for ingesting and managing large datasets and develop tooling for model optimization and backtesting.

The role partners with quantitative researchers to test hypotheses, generate data-driven trading signals, and enhance the performance of research workflows in a real-time

Qualifications

• Advanced degree and 8+ years in ML/Research/AI infrastructure.
• Strong Python and ML framework expertise.
• Experience designing end‑to‑end ML pipelines and deployment.
• Familiarity with CI/CD, testing, observability, and cloud platforms.

Responsabilités

• Design and evolve a scalable ML research framework covering experimentation to production deployment.
• Build reusable infrastructure for large, diverse datasets.
• Develop tooling for model optimization, backtesting, and performance analysis.
• Collaborate with quantitative researchers to test hypotheses and generate trading signals.
• Improve scalability and reliability of research workflows and deployment pipelines.

Connaissances

Python

ML Engineering

Distributed Systems

Mathematical Foundations

Formation

Master's or PhD in CS/ML/Statistics

Outils

PyTorch

NumPy

Pandas

Polars

Ray

Docker

Kubernetes

Description du poste

I am partnering with a leading proprietary trading and HFT firms to hire a Senior Research Engineer where machine learning, large-scale data, and research engineering sit at the core of the business. This is an opportunity to build the infrastructure and tooling that enables researchers to develop, train, and deploy data-driven models used in a real-time decision-making environment.

The role is well suited to experienced ML engineers or research engineers from large technology companies, AI organisations, or other data-intensive industries. Prior experience in financial services or trading is not required.

What You'll Do

Design and evolve a scalable machine learning research framework that supports the full lifecycle of model development, from experimentation and training through evaluation and production deployment.

Build reusable infrastructure for ingesting, transforming, combining, and managing large and diverse datasets in a systematic, data-driven way.

Develop tooling for model optimisation, supervised learning workflows, backtesting, experimentation, and performance analysis.

Partner closely with quantitative researchers to test hypotheses, develop predictive models, and generate data-driven trading signals for production systems.

Improve the scalability, reliability, and performance of research workflows, distributed training, and model deployment pipelines.

Contribute to engineering best practices across testing, CI/CD, observability, and platform reliability.

What We're Looking For

Master's or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or another highly quantitative discipline.

8+ years of experience in Machine Learning Engineering, Research Engineering, MLOps, AI Infrastructure, or a similar engineering role.

Strong mathematical and statistical foundations, with experience building data-driven predictive models.

Excellent Python programming skills and hands-on experience with libraries such as PyTorch, NumPy, Pandas, Polars, Ray, or equivalent ML/distributed computing frameworks.

Experience designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, evaluation, deployment, and monitoring.

Experience building scalable infrastructure for experimentation and model lifecycle management in production environments.

Familiarity with modern software engineering practices including version control, automated testing, CI/CD, Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).