FULLTIME
Freelance Computer Vision Engineer Edge AI
neuraldynamix
Not specified · onsite · Posted 9d ago
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Section · 01
About this role
Experience: Fresher / 0–2 years
Company: Stealth-mode AI Startup
Work mode: Remote / India
Domain: Computer Vision · Edge AI · Embedded AI
We are a stealth-mode AI startup building production-ready computer vision systems for real-world edge devices. We are looking for a strong fresher or early-career engineer who enjoys building complete computer vision applications rather than only training models.
You will work on the computer vision and application layer around modern edge AI platforms, cameras, perception models, and optimized inference runtimes. The role involves taking camera input, running perception models efficiently on edge hardware, processing model outputs, and converting them into reliable application-level results.
You may also get exposure to:
- Vision-language models
- 3D computer vision
- Depth cameras
- ROS/ROS2
- Robotics perception
- GPU acceleration
- Multi-camera systems
- Embedded AI deployment
Technologies
You do not need experience with everything listed below.
Core Skills:
- Python
- C++ basics
- OpenCV
- PyTorch
- NumPy
- Linux
Useful Experience:
- Edge AI devices
- CUDA / GPU acceleration
- TensorRT or other optimized inference runtimes
- ONNX / ONNX Runtime
- NVIDIA Jetson
- NVIDIA DeepStream
- Docker
- Git
- REST APIs
- GStreamer
- ROS / ROS2
What we are looking for,
We care significantly more about your ability to build things than your years of experience.
A strong candidate should:
- Have good fundamentals in computer vision
- Be comfortable programming in Python
- Understand basic C++
- Understand how modern object detection and segmentation models work
- Know basic image processing using OpenCV
- Be comfortable working in Linux
- Be curious about edge AI and hardware
- Be willing to debug problems across models, software, and hardware
- Be able to independently learn unfamiliar technologies
You Are a Strong Fit If You Have Built Something,
- Real-time object detection applications
- YOLO, RT-DETR, or similar detection projects
- Segmentation applications
- Object tracking systems
- Edge AI deployment projects
- TensorRT or ONNX inference pipelines
- Multi-camera applications
- Robotics perception projects
- OpenCV applications
- GPU/CUDA projects
- Final-year projects involving real-time computer vision
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Section · 02