1 день назад
Machine Learning Engineer (Quantitative Trading)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Machine Learning Engineer (Python/ML Infrastructure): Architecting and developing a scalable research platform for large-scale experimentation, model training, and simulation across on-premises HPC and multi-cloud environments with an accent on distributed training, GPU workloads, and reproducible experiment management. Focus on optimizing resource scheduling, improving observability, and building data and backtesting tooling for increasingly complex quantitative research models.
Location: Singapore, Singapore; hybrid working opportunities available.
Company
Quantitative trading firm developing electronic trading infrastructure, research platforms, and machine learning systems for independent trading teams.
What you will do
- Architect and develop a scalable, reliable, observable, and reproducible machine learning research platform.
- Build infrastructure for large-scale experimentation, model training, and simulation across on-premises HPC and multi-cloud environments.
- Design and optimize distributed training pipelines for high-throughput, GPU-accelerated workloads.
- Improve experiment management, model versioning, artifact tracking, and data lineage.
- Develop feature engineering, dataset generation, and large-scale backtesting tools.
- Improve compute efficiency, resource scheduling, workload isolation, and observability for ML workloads.
Requirements
- 2+ years of experience designing and building large-scale distributed systems.
- Strong Python programming skills with a focus on clean, maintainable, and high-performance code.
- Experience operating applications on Linux-based HPC clusters and/or cloud platforms.
- Understanding of distributed computing, parallel processing, and resource management.
- Experience with GPU workloads, modern ML frameworks such as PyTorch, TensorFlow, or JAX, and large-scale data pipelines.
- Strong troubleshooting, independent working, communication, and collaboration skills with researchers or data scientists.
Nice to have
- Experience building internal ML platforms or research tooling at scale.
- Familiarity with experiment tracking, workflow orchestration, and model lifecycle management.
- Experience with Docker and Kubernetes.
- Exposure to quantitative finance, simulation systems, or latency- and performance-sensitive domains.
Culture & Benefits
- Generous paid time off policies.
- Savings plans and financial wellness tools available by region.
- Free breakfast, lunch, and snacks in the office.
- Wellness experiences, wellness expense reimbursement, sports teams, and fitness events.
- Volunteer opportunities, charitable giving, social events, and celebrations.
- Workshops and continuous learning opportunities.
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