onsite
Machine Learning Engineer, Supply Chain Forecasting
Machine Learning Engineer, Supply Chain Forecasting
The Machine Learning Engineer, Supply Chain Forecasting will lead the research, development, and implementation of algorithms for supply chain scenarios, focusing on intelligent decision-making solutions based on large models. This role involves applying large model technologies to business scenarios, including sales forecasting, replenishment planning, capacity planning, and inventory allocation, to enhance operational efficiency and deliver business value.
About the role
About Our Team
Our team is missioned for the research, development, and implementation of algorithms for core supply chain scenarios. Focus on building intelligent decision-making solutions for the supply chain based on large models, encompassing core operations such as sales forecasting, replenishment planning, capacity planning, and inventory allocation. Simultaneously, explore the application of algorithmic technologies in more business areas to ensure the effectiveness of algorithm implementation and deliver business value.
Responsibilities
- Lead the research, development, implementation, and iterative improvement of algorithms related to multi-scenario supply chain forecasting, replenishment, capacity, and allocation.
- Apply large model technologies to business scenarios, demonstrating proficiency in the design and practical implementation of key techniques including Pre-training (PT), Continuous Pre-training (CPT), Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and AI Agents.
- Possess an application-oriented mindset, collaborate with business teams to realize the value of algorithms, and continuously explore new opportunities to enhance operational efficiency and manpower productivity.
Minimum Qualifications
- Bachelor's degree or higher in Computer Science, Mathematics, Artificial Intelligence, or related fields. Solid foundational knowledge of machine learning and hands-on experience with algorithms in real-world business scenarios are required.
- Expert in the practical application of large models in the industry, with proven experience in SFT, RL, and Agents. Familiarity with training frameworks like LLaMA Factory is essential.
- Proficient in Python and deep learning frameworks such as PyTorch or TensorFlow. Strong problem-solving skills are a must.
- Strong sense of responsibility and a commitment to continuous learning, with the ability to stay abreast of cutting-edge large model technologies and apply them to business problems.
Preferred Qualifications
- Published work in conferences (KDD, NeurIPS, ICML, SIGIR, WSDM, WWW, AAAI, IJCAI, RecSys, etc.) or success in ML competitions.
- Passion for building agentic systems that drive real-world business outcomes.