Life at Grab

At Grab, every Grabber is guided by The Grab Way, which spells out our mission, how we believe we can achieve it, and our operating principles - the 4Hs: Heart, Hunger, Honour and Humility. These principles guide and help us make decisions as we work to create economic empowerment for the people of Southeast Asia.

Get to know the Team

The Data Science (Geo Vision) team at Grab focuses on improving the maps and building map-based intelligence such as localization, routing, travel time estimation, traffic forecasting, that assist various Grab services like transportation, logistics, and pricing. We extensively use Computer Vision, NLP, Information Retrieval, and Text Mining along with conventional machine learning methods on a variety of signals including images, videos, text, sensor readings, GPS probes, etc to understand our locations and road networks. We also support the development of innovative, highly scalable, models through deep research and advanced analysis so that we make our products intelligent and delight our customers. We foster a culture where we enjoy raising the bar constantly for ourselves and others, and that strongly supports the freedom to explore and innovate.

Get to know the Role

Design, implement, and optimize machine learning algorithms for real-time embedded systems.

The Day-to-Day Activities

• Develop computer vision algorithms for object detection, instance segmentation and tracking

• Work closely with hardware engineers to integrate machine learning models into embedded platforms.

• Utilize Qualcomm's Snapdragon Neural Processing Engine SDK for model optimization and deployment.

• Collaborate with cross-functional teams to define product requirements and specifications.

• Conduct performance analysis and fine-tune models for optimal execution on embedded systems.

• Stay up-to-date with the latest advancements in machine learning and computer vision technologies.

The Must-Haves

• Bachelor's degree in Computer Science, Electrical Engineering, or related field.

• Minimum of 3 years of experience in embedded systems and machine learning.

• Strong programming skills in Python and C/C++.

• Hands-on experience with machine learning frameworks like TensorFlow or PyTorch.

• Proven track record in deploying machine learning models on embedded devices

The Nice-to-Haves

• Experience with Qualcomm's Snapdragon Neural Processing Engine SDK.

• Familiarity with hardware accelerators like GPUs, DSPs and TPUs.

• Proficient in system-level software, in particular hardware-software interactions and resource utilization.

• Knowledge of optimization techniques for resource-constrained environments.

Our Commitment

We are committed to building diverse teams and creating an inclusive workplace that enables all Grabbers to perform at their best, regardless of nationality, ethnicity, religion, age, gender identity or sexual orientation and other attributes that make each Grabber unique.