Summary:

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.

Summary:

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

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.

Computer Vision Engineer

What we know about this role

Skills required

Job description

Summary:

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Computer Vision Engineer

What we know about this role

Skills required

Job description

Summary:

Tools to land this role

Ground Reality

Spot the dealbreakers in this 426-word post before you apply

Insider Path

Find someone who can refer you here

Get tomorrow's jobs before you have to search

Someone shared one job with you. ASAI keeps finding the rest.