Get more replies from employers
Send a job-specific resume in minutes.
ROPEDIA PTE. LTD. is seeking a founding researcher to lead model design in world and 3D/4D perception for real-world applications. You will work across egocentric, multi-view, and multimodal data, coordinating with engineering and hardware teams to deliver reusable research code and internal tools.
You will co-design data collection protocols, evaluation metrics, and ablation studies, and you will produce research papers, internal reports, and demo systems to showcase progress at key milestones.
About Us
Ropedia is a Singapore-based, early-stage AI startup focused on building the data infrastructure for spatial and physical intelligence. Our core team brings together expertise from Tsinghua, Meta, Google, and UC Berkeley, and we have already achieved early-stage commercial viability and backed by top-tier VC and angel investors from Google, Nvidia, Amazon and etc.
We develop proprietary wearable and mobile capture devices and models to collect, reconstruct, and perceive multimodal human Xperience in real-world scenarios. Our mission is to power the next generation of World Models and Embodied AI. We offer compensation competitive with top-tier tech giants, along with significant early-team equity and incentives.
Role Overview
Location: Singapore
Type: Full-time
Job Description
You will join the founding research teamand lead model design and research in core areas such as world models, 3D/4Dperception, and egocentric understanding. Multiple research tracks are openunder this role, and candidates may match one or more directions based on theirbackground.
Focus: Human/hand/face modeling, motion and deformation priors, human–object interaction, and affordance modeling.Preferred Background: Experience in human pose/shape estimation, SMPL-type models, motion capture, or motion generation.
Focus: Multi-view and dynamic scene reconstruction, NeRF/Gaussian Splatting, novel view synthesis.Preferred Background: Experience in 3D reconstruction or neuralrendering; familiarity with camera calibration and bundle adjustment (BA).
Focus: Egocentric action and intention understanding, hand–object interaction, gaze/attention modeling, and task structure modeling.Preferred Background: Experience in video understanding, action recognition, or egocentric vision.
Focus: Long-term consistent 3D/4D scene mapping, scene graphs, object- and space-centric representations, and spatial reasoning.Preferred Background: Experience in large-scale mapping, semantic reconstruction, or agent world models.