Awign
Website:
awign.com
Job details:
About This Job
Awign
Location: Bengaluru, Karnataka, India
Work Mode: On-site
Industry: Robotics Engineering
Job Description
We’re looking for a Senior Robotics Perception Engineer to build end-to-end spatial perception systems that combine multi-camera vision, IMU data, and learning-based models into a unified 3D understanding of the world.
You’ll work on problems spanning SLAM, 6DoF pose estimation, multi-device sensor fusion, and calibration, while also leveraging modern computer vision and ML techniques (e.g., monocular depth, action/skill understanding, VLM).
This role sits at the intersection of robotics, 3D computer vision, and applied ML.
What You’ll Work On
Build real-time perception pipelines combining:Multi-camera systems (head-mounted + wrist-mounted cameras)IMU + RGB fusion for accurate camera pose estimationDevelop and optimize SLAM / visual-inertial odometry (VIO) systemsDesign multi-device sensor fusion to align multiple viewpoints into a single sceneImplement 3D / 6DoF hand and object pose estimation from RGB / RGB-D inputsImplement object detection modelsWork on stereo + multi-view geometry pipelinesBuild robust camera calibration systems:Intrinsics / extrinsicsCross-device calibrationIntegrate or research ML models for:Monocular depth estimationAction / skill labelingVLM systemsOptimize pipelines for real-time performance and robustness
What We’re Looking For
Core Skills (Must-Have)Strong foundation in 3D Computer Vision & GeometryMulti-view geometry, epipolar geometry, transformationsExperience with SLAM / VIO / sensor fusionVisual SLAM, visual-inertial fusion, state estimationHands-on experience with camera calibrationIntrinsics, extrinsics, stereo calibrationExperience working with multi-camera systemsStrong programming skills in C++ and/or Python
Good to Have (High Impact)
Experience with hand pose / human pose estimation (2D/3D/6DoF)Familiarity with RGB-D / depth sensorsExperience with learning-based vision modelsMonocular depthPose estimationAction recognitionExposure to VLM or embodied AI systemsExperience optimizing for real-time systems (latency, memory, throughput)Familiarity with frameworks like:OpenCV, PyTorch, ROS, COLMAP, ORB-SLAM, OpenVINS, etc.
Nice to Have (Bonus)
Experience with multi-device synchronization (time alignment, sensor clocks)Background in robotics, AR/VR, or embodied AI systemsExperience deploying models on edge devices / mobile systems
What Makes This Role Unique
You’ll work on complex multi-sensor setups (not just single-camera CV)Ownership of end-to-end perception stack (not just modeling or infra)Blend of classical geometry + modern MLOpportunity to shape next-gen embodied / spatial AI systems
Ideal Candidate Profile
Someone Who
Can move fluidly between math, systems, and MLIs comfortable debugging real-world sensor noise and calibration issuesHas built or worked on real-time perception systems, not just offline models
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