Senior Machine Learning Engineer

China·ShenzhenFull-timesenior

Machine Learning EngineerData

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Quick Summary

Overview

Our Journey The ShopBack Group is Asia-Pacific’s leading shopping, rewards, and payments platform, serving over 60 million shoppers across 13 markets. In 2025,

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Machine Learning EngineerData

The ShopBack Group is Asia-Pacific’s leading shopping, rewards, and payments platform, serving over 60 million shoppers across 13 markets. In 2025, the Group continued its global growth with its expansion into North America. Driven by the vision to make every day more rewarding, ShopBack is dedicated to saving members money and time, and delivering delight every day. The platform also enables merchants and brands to engage with their members in a cost-effective manner. Founded in 2014, ShopBack now powers over US$5.5 billion in annual sales for over 20,000 online and in-store partners, and has rewarded shoppers with more than US$800 million (over S$1 billion) in Cashback to date. Through its innovative offerings, ShopBack continues to create value for both members and merchants. Notably, its payment solution, ShopBack Pay, offers members a convenient and rewarding payment option at checkout.

Utilize Machine Learning: Enhance our products and improve the shopping experience for both users and merchants. This includes leveraging machine learning in areas such as recommendations, personalization, risk, fraud detection, search, and more.

Subject Matter Expertise: Be a subject matter expert in a specific ML domain while maintaining a solid understanding of product and user challenges.

Handle Ambiguity: Navigate and resolve ambiguous problem definitions effectively with or without dedicated Product Manager support.

Metrics Driven: Understand critical business and product metrics, apply the right tools and technologies, and drive efforts to positively influence those metrics.

Experimentation Culture: Foster a fast-paced, high-iteration experimental culture to test and refine solutions quickly.

Collaboration: Work effectively with cross-functional partners and stakeholders to set and achieve optimal outcomes.

Curious: Interest in exploring and learning the latest technologies in the industry.

Significant prior success as a Machine Learning Engineer working on challenging problems at scale

6+ years of industrial ML experience, with expertise in modeling and statistical modeling

Good understanding of data collection, aggregation, analysis, visualization, productionisation, and monitoring of ML products - aka MLOps

Ability to develop ML Products with good engineering practice and mindset

Strong desire to solve tough problems with scientific rigour at scale

An understanding of the value derived from getting results early and iterating

Education in a quantitative field such as Computer Science, Operations Research, Statistics, Econometrics or Mathematics

Strong skills in Python and machine learning and deep learning libraries (Pytorch and Tensorflow)

Passion to answer Product/Engineering questions with data

Demonstrated use of AI tools (e.g. ChatGPT, Cursor) to develop new or improve workflows, enhance productivity, and drive efficiency at scale.

Agency - We take ownership and act, rather than waiting for permission. When something's blocking progress, we find a way through it and follow through until it's done.
Judgement - We aim for high-impact decisions, not just easy wins, and we put the bigger picture ahead of individual interests. That means moving quickly and confidently, while staying thoughtful about when a call really matters.
Learning Velocity - We pick up new skills fast and let go of old habits just as quickly when something better comes along. We benchmark ourselves against the best and keep raising our own bar.
Tenacity - We stay in it when things get hard, keeping a level head under pressure. We debate openly before deciding, then commit fully — and support each other along the way.

Career growth opportunities to take on greater challenges that help you realise your ambitions.
Be part of a winning team on a journey to global scale.
Competitive compensation based on performance.
Candid, open, and collaborative culture where feedback is valued.