Senior Machine Learning Engineer

About the Company:

Our client is a high-growth, international digital freight and logistics technology platform backed by a prominent, US-listed digital transportation leader. By leveraging advanced AI, cloud infrastructure, and predictive analytics, they build intelligent marketplaces that connect global shippers directly with freight networks to enable seamless, closed-loop transactions.

As part of their rapid international expansion across Southeast Asia and the Middle East, they are establishing their core global technology hub in Singapore. This is a rare opportunity to join an agile team building high-concurrency, cross-border infrastructure completely from scratch.

Key Responsibilities:

• Core Engine Optimization: Oversee the end-to-end design, refinement, and execution of automated platform allocation logic, intelligent supply routing, and real-time dynamic valuation models.
• Predictive Pipeline Engineering: Autonomously drive the entire lifecycle of complex data systems—including spatial asset tracking data, predictive behavior modeling, feature development, and robust containerized production deployments.
• Data-Informed Diagnostics: Dive deep into platform transaction logs and user operational flows to isolate algorithmic friction or drop-offs, executing strategic changes to maximize user lifetime value and platform conversion.
• Cross-Border Localization: Partner closely with global technology centers to adapt, fine-tune, and scale core algorithmic frameworks for highly fragmented international markets.

Requirements:

• Education: Bachelor's degree or higher in a highly quantitative discipline (e.g., Computer Science, Data Science, Operations Research, Applied Mathematics, or Statistics).
• Experience: Minimum 2+ years of commercial experience in applied machine learning, with a proven track record in high-throughput architectures (e.g., Personalized Recommendation Engines, Search Discovery, Programmatic Advertising/AdTech, or High-Frequency Asset Dispatch/On-Demand Networks).
• Target Domain Competency (Strong foundation in at least two fields):
• Geospatial Data Engineering & Time-Series Analysis (applicable for route planning, asset tracking, and arrival metrics estimation).
• Statistical Causal Inference & Uplift Frameworks (applicable for user growth mechanics, targeted campaigns, and dynamic incentive optimization).
• Mathematical Optimization & Reinforcement Learning (applicable for complex supply-demand balancing and elastic pricing frameworks).
• Technical Stack: Deep, hands-on production experience with dominant deep learning frameworks (e.g., PyTorch or TensorFlow).
• Core Traits: Strong analytical rigor, acute sensitivity to data trends, and a resilient mindset geared toward solving open-ended problems in an agile environment.

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