Computer Vision Engineer

We are looking for a Systems-First Computer Vision Engineer who specializes in the "Last Mile" of AI: taking a model and making it run continuously, reliably, and instantly on live video feeds. This role is not about training models in a notebook; it is about building the high-performance highways (Pipelines) that allow Vision AI to run in the real world. You will architect robust streaming architectures using GStreamer/RTSP and optimize inference for ultra-low latency on Edge and Cloud environments.

Key Responsibilities
Architect Streaming Pipelines: Design and implement robust, real-time video ingestion pipelines handling multiple RTSP streams using tools like GStreamer, FFmpeg, and WebRTC.
Inference Integration: Take trained models from the ML team and integrate them into production pipelines. Your goal is to ensure the model runs stable, fast, and without memory leaks.
Latency Optimization: Obsess over milliseconds. Optimize data processing pipelines to ensure low-latency inference on both Edge devices (NVIDIA Jetson) and Cloud servers.
Fault Tolerance: Build "Crash-Proof" systems. Ensure that if a camera goes offline or a frame is dropped, the system recovers gracefully without manual intervention.
Framework Evolution: Maintain and evolve our proprietary vision framework by writing modular, reusable, and efficient Python code/libraries.
Performance Engineering: Diagnose bottlenecks in the system—whether it's CPU, GPU, or Network—and implement architectural fixes.

Skills & Requirements
Video Engineering Mastery: Deep expertise in video streaming protocols (RTSP, WebRTC, FastRTC) and processing tools (FFmpeg, GStreamer). You know how to handle frame buffers, decoding, and encoding efficiently.
Core Vision Stack: extensive experience with OpenCV and Image Processing fundamentals. You understand geometry, color spaces, and pixel-level manipulation.
Production Python: Strong experience writing fault-tolerant, multi-threaded/async code. You understand how to manage resources in long-running processes.
Deployment Native: Hands-on experience with Docker is mandatory. You know how to containerize a complex vision application with all its dependencies.
Mathematical Foundation: Command over geometry and statistics for designing complex logic layers on top of model detections.

Brownie Points
Hardware Acceleration: Experience with NVIDIA TensorRT, DeepStream, or Triton Inference Server for maximizing GPU throughput.
Framework Knowledge: Familiarity with PyTorch/TensorFlow runtimes (strictly for inference and loading models).
DevOps Awareness: Understanding of Kubernetes orchestration and CI/CD pipelines.
Data Handling: Experience with SQL/NoSQL databases for storing metadata and analytics results.

What We Offer
Meritocracy: A candid startup culture where the best ideas win.
The Playground: Access to the latest NVIDIA Hardware and cutting-edge Generative AI tools.
Ownership: Lead a performance-oriented team driven by autonomy and open to experiments.
Impact: Design systems for high accuracy and scalability that physically move the global supply chain.