Machine Learning Engineer

XNO is a quantitative AI technology company building institutional-grade financial infrastructure for professional investors across Vietnam and Southeast Asia. We operate at the intersection of systematic trading, AI, and financial data — serving fund managers, trading desks, and wealth professionals with tools they actually depend on for live decisions.

We’re looking for a Machine Learning Engineer who builds models that ship — not notebooks. What you train and deploy here feeds directly into research and live trading systems, so correctness, robustness, and knowing when a model is wrong are not optional.

Responsibilities

• Build, train, and deploy ML models that power trading and research systems — from feature engineering to production-grade inference

• Design and maintain ML pipelines: data ingestion, feature stores, training, validation, and deployment

• Collaborate with quant researchers to productionize alpha models and signals into live systems

• Optimize model performance for latency, throughput, and reliability in live trading environments

• Build evaluation frameworks to validate models against real financial outcomes, not just offline metrics

• Monitor models in production: drift detection, retraining triggers, and accuracy tracking

Requirements

• 2+ years building and shipping production ML systems — not research projects

• Strong Python skills (NumPy, Pandas, Scikit-learn, PyTorch)

• Solid understanding of statistics, model evaluation, and overfitting/generalization tradeoffs

• Experience with ML pipelines: feature engineering, model versioning, and deployment

• Production mindset: you instrument everything, handle failures gracefully, and know when a model is wrong before users do

Bonus points

• Experience working with financial or trading data

• Experience with reinforcement learning

• Familiarity with low-latency or high-throughput inference systems

Benefits

• Competitive salary + performance bonus

• Full ownership of ML systems you build — from data to deployment

• Direct collaboration with quant researchers and institutional finance professionals on live systems

• Clear growth path as XNO scales across Southeast Asia