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.

• 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.

• 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).

We welcome candidates from technology companies, AI research organisations, cloud platforms, autonomous systems, recommendation systems, search, computer vision, NLP, or other large-scale machine learning environments. Candidates with strong machine learning infrastructure and research engineering experience from outside financial services are encouraged to apply.

"Sanderson-iKas" is the brand name for the following companies incorporated in Hong Kong: Sanderson Solutions International (Hong Kong) Limited (Business Registration no.53741924) and iKas International (Asia) Limited (Business Registration no.39818987)

Website: www.sanderson-ikas.hk