Senior Research Engineer

Sanderson-Ikas; Hong Kong; On-site; HKD 900,000 - 1,200,000; Full time

Job summary

Sanderson-Ikas is partnering with a leading proprietary trading and HFT firm to hire a Senior Research Engineer. You will design and evolve scalable ML research frameworks, build data ingestion and management infra, and develop tooling for model optimization and deployment in a real-time environment.

You’ll collaborate with quantitative researchers to test hypotheses, generate data-driven trading signals, and enhance research workflows, pipelines, and platform reliability using Python, PyTorch,

Qualifications

• Master’s or PhD in Computer Science, ML, statistics, mathematics, physics, or a highly quantitative field.
• 8+ years of experience in Machine Learning Engineering, Research Engineering, MLOps, AI infrastructure, or similar roles.
• Strong mathematical and statistical foundations with data-driven modeling experience.
• Excellent Python programming; hands-on with PyTorch, NumPy, Pandas, Polars, Ray, or equivalent ML/distributed frameworks.
• Experience designing end-to-end ML pipelines: preprocessing, training, deployment, monitoring.
• Experience building scalable infrastructure for experimentations and model lifecycle in production.
• Familiar with version control, CI/CD, Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP).

Responsibilities

• Design and evolve a scalable ML research framework for model development lifecycle from experiment to production deployment.
• Build reusable infrastructure to ingest, transform, and manage large, diverse datasets.
• Develop tooling for model optimization, supervised workflows, backtesting, experimentation, and performance analysis.
• Collaborate with quantitative researchers to test hypotheses and generate data-driven trading signals for production systems.
• Improve scalability, reliability, and performance of research workflows, distributed training and deployment pipelines.
• Contribute to engineering best practices across testing, CI/CD, observability, and platform reliability.

Skills

Python programming

Education

Master's or PhD in Computer Science/ML/Statistics

Tools

PyTorch

NumPy

Pandas

Polars

Ray

Docker

Kubernetes

AWS

Azure

GCP

Job description

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).