Computer Vision Engineer

Description :

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 systemwhether it's CPU, GPU, or Networkand 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.

• Mandatory Hardware Willingness : Computer vision and Python developers will not just write code. They must be willing to research and validate hardware (e.g., optimal camera placement, server sizing, safety analysis, cost-effectiveness).

• Rejection Criteria : Reject candidates who demand 100% software work. Candidates seeking a 60-70% software split are a good fit.

• Scope of Hardware Work : It does not mean building camera internals. It means understanding specs (RAM, processor, megapixels, focal length) to determine the right fit for the application, similar to checking specs before buying a smartphone.

• Team Collaboration : Candidates won't make hardware decisions entirely on their own. They are expected to research and collaborate with leads/product teams to make recommendations.

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.

• RTSP Protocol : Experience with RTSP is a "good to have" but not mandatory.

• Real-time Experience over Streaming : If a candidate has no direct experience with camera streaming but has built real-time applications/pipelines, they should still be considered and interviewed.

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.