puestos de Machine Learning en Singapur

We are looking for experienced Quantitative Developers to build and scale the core infrastructure behind our systematic trading strategies. Our systems blend real-time data processing, machine learning, and agent-based technologies to power intelligent, automated workflows across diverse trading environments.

This role focuses on developing platforms that support research, strategy automation, and data integration at scale. Ideal candidates may come from backgrounds in data engineering, ML infrastructure, distributed systems, or intelligent agent development.

Responsibilities

• Build infrastructure for agent-powered systems that automate research, signal generation, and strategy execution.
• Design scalable, low-latency data pipelines aggregating real-time information from multiple trading venues.
• Develop LLM-integrated tooling for intelligent research and strategy orchestration.
• Collaborate with researchers to implement models, strategies, and portfolio management logic.
• Integrate various APIs and data sources into a unified research and execution framework.
• Develop modular systems optimized for deployment across multiple platforms and strategies.
• Create internal tools and dashboards for analytics, scenario testing, and performance attribution.

Qualifications

• Strong programming and software engineering skills (Python preferred); experience with ML ops, distributed systems, or real-time applications is a plus.
• Familiarity with agent tooling, simulation environments, or data-intensive backtesting frameworks.
• Experience developing trading or portfolio systems with multi-venue data and execution requirements.
• Proven track record of shipping robust, production-grade infrastructure in a fast-paced environment.
• Comfort working with APIs, real-time data streams, or protocol-level data sources.
• Exposure to smart contract interaction, autonomous workflows, or decentralized execution environments is a bonus.
• Prior experience supporting live strategy deployment and automation is strongly preferred.