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Job details:
Position Title: Real-Time Computer Vision & Edge AI Engineer (Founding Engineering Team / Core LLD)
Reporting Structure: High-Level AI Architect (Principal ML Scientist, Google)
Domain: Sub-16ms Edge AI, 3D Pose & Shape Estimation (SMPL-X), TensorRT C++ Inference, Zero-Copy Systems
Performance Benchmark: Hard locked 60 FPS (<16.6 ms total frame budget) on dedicated RTX hardware
1. Position Overview & Architecture
We are building a proprietary, ultra-low-latency spatial computing platform centered on high-fidelity 100% 3D Digital Twin architecture and real-time human digitization.
In this role, you will serve as the Low-Level Design (LLD) Core AI Engineer, working directly alongside a Lead AI Scientist from Google. Your primary mandate is to solve complex surface occlusion and volumetric estimation challenges by building an ultra-fast C++ inference pipeline. This system must accurately regress a subject's true underlying 3D body shape and skeletal pose directly from a live camera feed. You will deploy models that extract parametric data (SMPL-X shape/pose parameters) and bridge these joint rotations seamlessly into our Vulkan graphics engine via shared GPU memory.
System Architecture:
● Hardware Camera Ingestion: (V4L2 / GStreamer / CUDA)
↓ Raw RGB Frames (Zero CPU Copy)
● Edge AI Inference: (TensorRT / ONNX C++ API for 3D Pose Tracking, Kinematic Anchoring, SMPL-X Shape)
↓ 3D Skeletal Transforms & Shape Parameters
● Zero-Copy Shared Memory: (CUDA-Vulkan Bridge feeding directly into OpenRigLogic / MetaHuman Engine)
2. Key Responsibilities & Deliverables
A. Real-Time 3D Pose & Shape Estimation
● Deploy and optimize state-of-the-art 3D human body reconstruction models (e.g., Shapy, SMPLify-X, CLIFF) to accurately regress the user's underlying skeletal structure and body volume, effectively bypassing unpredictable surface topologies and complex environmental occlusions.
● Extract mathematically stable shape parameters (β) and pose parameters (θ) to drive the skeletal hierarchy of a high-fidelity digital avatar.
B. Edge Inference Pipeline (TensorRT)
● Translate Python-based research models into production-grade C++ inference engines using NVIDIA TensorRT and ONNX Runtime.
● Implement INT8/FP16 quantization, layer fusion, and custom CUDA plugins to ensure the entire AI inference pass executes within a strict <10 ms budget per frame.
C. Temporal Smoothing & Anti-Jitter Kinematics
● Implement highly optimized temporal filters (Kalman filters, One-Euro filters, optical flow tracking) in native C++ to eliminate all high-frequency jitter from the output joint rotations before they reach the graphics engine.
● Ensure kinematic constraints (e.g., fixed bone lengths) are strictly maintained to prevent the digital asset from stretching or warping dynamically.
D. Zero-Copy Ingestion & Engine Synchronization
● Build hardware-accelerated video capture pipelines using V4L2 or GStreamer to ingest raw camera frames directly into GPU memory.
● Bridge the output coordinate data and transformation matrices to the graphics team using POSIX shared memory and CUDA-Vulkan interop (VK_KHR_external_memory_fd), eliminating CPU staging overhead.
3. Technical Qualifications & Tech Stack
● Core Programming: Production-level Modern C++ (C++17/20), Python (strictly for model training/validation), and CUDA C/C++.
● AI & Acceleration Frameworks: NVIDIA TensorRT, ONNX Runtime (C++ API), PyTorch.
● Computer Vision Libraries: OpenCV (CUDA backend), MediaPipe C++ bindings.
● Mathematical Foundations: 3D Kinematics, Matrix Transformations, Quaternions/Euler angles, statistical body modeling (SMPL/SMPL-X architecture).
● Systems Architecture: Low-latency memory management, multi-threading (std::jthread, lock-free queues), SIMD vectorization.
4. Relevant Projects & Demonstrable Experience (Preferred)
Candidates will be preferred if they present functional codebases, GitHub repositories, or thesis work covering:
● Real-Time Body Fitting / Pose Estimation: Practical experience deploying 3D human pose or shape reconstruction models on live video feeds.
● TensorRT / C++ Deployment: Demonstrable experience stripping a PyTorch model out of Python and running it natively in C++ using TensorRT or ONNX, ideally with custom CUDA layers or INT8 calibration.
● High-Throughput Vision Pipelines: Built a C++ video processing pipeline that aggressively minimizes latency and avoids memory garbage collection pauses.
● Kinematics & Smoothing: Applied mathematical filters to raw sensor or AI data to produce smooth, mechanically accurate 3D rotations.
5. Compensation & Engagement Structure
● Compensation: ₹1,00,000 to ₹2,00,000/month
● Mentorship: Direct architectural guidance, algorithm review, and technical leadership from a Principal ML Scientist at Google.
● Hardware: Dedicated high-end workstation equipped with discrete NVIDIA RTX hardware.
HOW TO APPLY?
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