Senior Algorithm Engineer / Algorithm 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, spatial-temporal search matching, 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 engineering pipelines (Spark/Flink), and robust containerized production deployments.
• Data-Informed Diagnostics: Dive deep into platform transaction logs and operational flows to isolate algorithmic friction or drop-offs, executing strategic model changes to maximize platform conversion and supply-demand efficiency.
• 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). Ph.D. or Master's is a strong plus.
• Experience: Minimum 2+ years of commercial experience in applied machine learning and algorithm design, with a proven track record in high-throughput architectures (e.g., High-Frequency Asset Dispatch/On-Demand Networks, Search Discovery, Personalized Recommendation Engines, or AdTech).
• Target Domain Competency (Strong foundation in at least two fields):
• Geospatial Data Engineering & Time-Series Analysis (route planning, asset tracking, ETA metrics).
• Mathematical Optimization & Reinforcement Learning (complex supply-demand balancing, elastic pricing, LP/MIP models).
• Statistical Causal Inference & Uplift Frameworks (user growth mechanics, dynamic incentive optimization).
• Technical Stack: Deep production experience with PyTorch or TensorFlow, alongside strong coding proficiency in Python, C++, Java, or Golang for low-latency model deployment.

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