Grab · Singapore, SG · about 2 months ago
Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility.
The Integrity team acts as the guardians of all our users on Grab. Our mission is to be the most trusted platform in the world for protecting individuals and businesses in the domain of fraud, risk, digital identity and safety. The data science team leverages rich datasets to build AI solutions to solve these tough problems. We're a hands-on team involved in the entire modelling lifecycle: from data exploration and model development to production deployment and model refresh.
We are looking for Senior Data Scientists specialising in Computer Vision and Multimodal LLMs post-training.
You'll focus on developing advanced models for precise OCR, KYC verification, and document/image information extraction. This work directly supports enhanced user onboarding, verification workflows, and several downstream applications. You'll use modern technologies, including Multimodal LLMs, to improve visual intelligence capabilities.
We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.
Headquarters
Singapore
Work Location
on-site
Job Category
Data Science / AI / Machine Learning
Application Deadline
Not specified
Job Type
full-time
Experience Level
senior-level
Application Method
Apply via Website
Salary
Not specified
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