Senior Manager, Machine Learning Engineering – Logistics Matching Systems at GoGoX

Hong Kong, Hong Kong Island, Hong Kong S.A.R. -

Full Time

Start Date

Immediate

Expiry Date

04 Mar, 26

Salary

0.0

Posted On

04 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Logistics, Data Models, Algorithms, Real-Time Systems, Collaboration, Communication, Technical Leadership, Data Architecture, A/B Testing, Model Experimentation, Documentation, Testing Best Practices, Operational Efficiency, Urban Logistics, Python

Industry

technology;Information and Internet

Description

We are building a smart logistics platform to power the reservation and rental of vans and trucks for goods delivery across Hong Kong. Our system matches delivery orders to available vehicles efficiently and intelligently, optimizing operational efficiency, driver experience, and customer satisfaction. As Senior Manager, Machine Learning Engineering, you will lead the architecture and development of the core machine learning system that powers our order-to-driver matching functionality. You will drive the design of data models, algorithms, and evaluation frameworks—ensuring they are robust, scalable, and tailored to the unique constraints of urban logistics in Hong Kong. While you may contribute technically, your primary role will be to guide the engineering team to deliver on this vision. You will work closely with product, devops, and operations teams to align technical solutions with real-world delivery workflows and business goals. What You Will Deliver Lead and grow a team of machine learning engineers building the core logistics matching engine Design data models and algorithms that assign goods delivery orders to vans and trucks efficiently, based on location, vehicle capacity, time constraints, and driver preferences Define and execute the ML roadmap, balancing business impact, technical feasibility, and experimentation speed Translate operational requirements into ML system architecture and model objectives Monitor and improve matching performance, including assignment success rate, delivery latency, fulfillment efficiency, and utilization Collaborate cross-functionally with Product, DevOps, and Operations to ensure solutions align with real-world use cases Guide team in model experimentation, A/B testing, validation, deployment, and iteration Establish metrics, dashboards, and processes to track system effectiveness, usage, and ROI Promote engineering excellence including reproducibility, documentation, and testing best practices Who You Are 5+ years of experience in applied machine learning, with at least 2 years in a technical leadership or management role Strong background in building ML systems for real-time logistics, dispatch, supply-demand matching, or dynamic marketplaces Demonstrated ability to design, evaluate, and deliver production ML systems Experienced in designing data models, pipelines, and algorithms in transportation or delivery contexts Familiarity with data architecture as it relates to supporting advanced data analytics and business intelligence tools Hands-on knowledge of ML tools and data platforms (e.g. Python, scikit-learn, TensorFlow/PyTorch, Spark, SQL, AWS/GCP) Excellent communication and collaboration skills with cross-functional stakeholders Strategic mindset with a focus on solving real-world operational problems Based in Hong Kong or willing to relocate to work closely with local stakeholders Bonus Points Familiarity with Hong Kong’s urban and transportation landscape Experience with dispatch optimization, constrained matching, vehicle routing problems, or reinforcement learning Experience managing remote or distributed engineering teams

Responsibilities

Lead and grow a team of machine learning engineers to build the core logistics matching engine. Design data models and algorithms for efficient assignment of delivery orders to vehicles, while collaborating with cross-functional teams to align technical solutions with business goals.