Algorithm Engineer

Highlights of This Role

1. Strong corporate backing: Backed by NYSE-listed Full Truck Alliance with stable financial status and robust risk resistance, far more reliable than ordinary startups.
2. Massive market potential: The global digital logistics market is worth trillions of dollars; digital penetration in emerging markets is rising rapidly, delivering enormous room for growth.
3. Proven operational business: Live products with real users, orders and transaction data in operation — no conceptual POC or unvalidated business plans.
4. Ample career development: As a core member of the global expansion team, you will enjoy abundant growth opportunities alongside fast-developing business.
5. Meaningful technical challenges: Complex cross-border scenarios covering multiple countries, time zones and currencies, featuring sparse data and volatile supply-demand balance, leaving extensive room for algorithm optimization.
6. Flexible working model: Based in Singapore with remote work allowed, supported by a cross-border collaborative team culture.

Job Responsibilities

1. Develop and iterate the company’s core matching transaction algorithms and marketing algorithms, driving sustainable improvement of relevant business metrics.
2. Empower business via algorithm optimization to continuously upgrade user experience and operational performance.
3. Independently deliver the full lifecycle of model R&D, including data processing, feature engineering, model training, deployment and monitoring.
4. Deeply engage with product and business teams, identify deficiencies in existing models and strategic mechanisms through data analysis, and propose & implement optimization solutions.
5. Track cutting-edge industry research, iterate and innovate algorithms tailored to business scenarios, and deploy technical innovations into live production.

Job Requirements

1. Bachelor’s degree or above in Computer Science or related majors; minimum 2 years of relevant working experience in recommendation, search, advertising or related fields.
2. In-depth understanding of machine learning fundamentals; hands-on experience in causal inference, time series forecasting, spatial-temporal data mining, multi-task learning, online learning, reinforcement learning, operational research optimization or related domains is highly preferred.
3. Solid programming skills with proficient command of mainstream machine learning and deep learning frameworks.
4. Genuine passion for algorithms, strong data sensitivity, logical thinking, self-motivation and a pursuit of excellence.
5. Excellent communication skills, quick learning ability, capacity to understand business and product logic, and user-centric mindset.
6. Strong data collection and analysis capabilities with sharp insight into data patterns.
7. Effective cross-team collaboration skills, strong execution and ability to work under pressure.