Grab
Technology
LeadEdgeAIEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“Lead Edge AI Engineer at Grab. Skills: Edge AI, PyTorch, Computer Vision, Embedded Android. Lead edge development. Develop multi-task learning models”
What You'll Achieve.
Maintain recording integrity; Ensure system stability during dynamic model graph reconfiguration
Industry & Context.
Work onsite
What They're Looking For.
Must Have
Minimum of 5 years of experience in computer vision and machine learning, Mandatory focus on Edge AI deployment and optimization, Deep expertise in PyTorch, Proficient in Video Action Recognition, Learning network design, Deep understanding of resource-constrained environments, Knowledge of general model optimization techniques for edge devices, Proficiency in English (speaking and writing)
Nice to Have
Context-Aware DSP Optimization & Dynamic Graph Execution, Experience designing and implementing intelligent runtime management logic on Android, Dynamic model switching or conditional computation on Android, Dynamically load/unload specific heads of multi-task architectures on the Qualcomm Hexagon DSP, Proven experience with Qualcomm DSP (Hexagon), SNPE (Snapdragon Neural Processing Engine), QNN SDK, Background in Android development for edge devices, Handling hardware-software interactions and resource utilization, Experience with camera localization and motion estimation using a combination of GPS, IMU, video, and magnetometer, Experience with advanced computer vision techniques, Camera localization, Motion estimation, 3D reconstruction, Proficiency in low-level system software, Hardware-software interactions on Qualcomm chipsets
What You'll Do.
Lead edge development
Develop multi-task learning models
Refine multi-task learning models
Develop video action recognition systems
Deploy Computer Vision algorithms
Conduct performance analysis
Reduce power consumption
Manage thermal constraints
Ensure minimal model switching latency
Implement safety mechanisms
Ensure system stability
How You'll Work.
Team & Collaboration
Collaborate with Firmware teams; Collaborate with Mobile teams; Integrate signals for model decision-making
Communication Scope
Present technical data insights
Full Job Description
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 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, and traffic forecasting. We use Computer Vision and conventional machine learning methods on a variety of signals—specifically utilizing edge device footage—to understand our locations and road networks. Get to know the Role We are looking for a Lead Data Scientist / Edge AI Engineer to lead edge development for our edge devices. A key focus will be Multi-Task & Action Recognition Development, where the successful candidate will be responsible for developing and refining multi-task learning models and video action recognition systems for our edge devices. You will work onsite and will report to the Head of Data Science based in the Cluj Office. The Critical Tasks You Will Perform * Multi-Task & Action Recognition Development: Develop and refine multi-task learning models (specifically Hydranet architecture) and video action recognition systems using PyTorch. * Edge Deployment & Engineering: Deploy Computer Vision algorithms into embedded Android platforms, utilizing the Qualcomm SNPE / QNN SDK to interact directly with the DSP. * Resource Efficiency: Conduct rigorous performance analysis to reduce power consumption and manage thermal constraints. You will ensure model switching latency remains minimal to maintain recording integrity. * System Stability: Implement safety mechanisms to ensure system stability during dynamic model graph reconfiguration. * Collaboration: Collaborate with Firmware and Mobile teams to integrate signals for model decision-making. ## Qualificat
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