Computer Vision Engineer - Video Streaming

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.

• Extensive experience with Redis & RDBMS

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• 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.