> Markdown version of [/jobs/ext/1242620-computer-vision-engineer-edge](https://www.wearedevelopers.com/jobs/ext/1242620-computer-vision-engineer-edge). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Computer Vision Engineer-Edge

- **Company:** Viatouch Media, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $88,295.0 - $135,000.0
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Bash Shell, C++ (Programming Language), Ubuntu (Operating System), Profiling, Software Quality, Nvidia CUDA, Software Debugging, Linux, Python (Programming Language), Unix Shell, Machine Learning, Object Detection, OpenCV, Tensorflow, Management of Software Versions, Pytorch, Deep Learning, Backend, Git, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT
- **Published:** July 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0f7148302e3316ab

## About the Role

* MS or PhD in Computer Science, Electrical Engineering, or related field with strong focus in computer vision / deep learning / machine learning (or equivalent practical experience).
 * 4+ years professional experience in Deep Learning / Computer Vision / Machine Learning with production responsibility.
 * Retail CV experience (required): hands-on experience training object detection models on retail products/objects (e.g., consumer packaged goods, SKU-level recognition, shelf/bin/basket scenes), including dataset design and iteration based on real-world failures.
 * Embedded/Edge experience: 1+ year deploying CV/ML to embedded devices, including performance constraints and production troubleshooting.
 * Proven ability to take models from training * deployment * evaluation * iteration in real environments.

Required Technical SkillsProgramming

 * Python (3.x) - training, experimentation, tooling, and/or production services
 * C++ (C++14/17) - performance-critical inference/pipeline code on-device
 * GPU fundamentals (CUDA-enabled stacks) - can reason about bottlenecks and optimize for GPU deployment on Jetson-class devices

(You don't need to write custom CUDA kernels daily, but you must be effective optimizing in a CUDA-enabled environment.)

 * Bash / Linux shell - device-level debugging, profiling, automation

Frameworks / Tooling

 * PyTorch and/or TensorFlow (PyTorch strongly preferred)
 * OpenCV
 * TensorRT, ONNX (conversion + runtime considerations)
 * Linux (Ubuntu-based stacks common for Jetson)
 * Git

Preferred (Strong Pluses)

 * Direct Jetson Orin / Xavier deployment experience + JetPack familiarity
 * DeepStream and/or GStreamer for real-time multi-stream video pipelines
 * YOLO-family, transformer-based detectors, lightweight/mobile architectures
 * Multi-object tracking (DeepSORT/ByteTrack-style) and real-world tuning
 * MLOps / experiment tracking / dataset versioning (W&B, MLflow, DVC, or equivalent)
 * Experience building systems robust to packaging changes, planogram drift, glare, motion blur, and occlusions

## Description

* Lead production computer vision pipelines running on Jetson Orin (JetPack-based Linux environment).
 * Build and stabilize a 3-4 camera capture + inference pipeline suitable for real-time retail use.
 * Drive practical multi-camera readiness:
 * Field-of-view coverage and camera placement tradeoffs
 * Calibration concepts (intrinsics/extrinsics), lens distortion handling, alignment checks
 * Multi-stream handling (throughput, dropped frames, pipeline stability)

Computer vision + machine learning (retail-first)

 * Research, design, train, and ship object detection + tracking models for retail environments:
 * SKU / product recognition, multi-item scenes, occlusions
 * Glare/reflective packaging, variable lighting, crowded bins/shelves
 * Own dataset evolution:
 * Collection strategy designed around multi-camera coverage and real failure modes
 * Labeling specs, QA processes, augmentation, hard-negative mining
 * Active learning loops based on production misses and edge cases
 * Benchmark models for accuracy + robustness + speed, and define acceptance criteria for rollout.

Edge deployment + performance engineering

 * Convert and optimize models for embedded deployment:
 * ONNX export and runtime considerations
 * TensorRT optimization (FP16/INT8 where appropriate)
 * Improve throughput/latency/thermals/reliability for long-running production operation.
 * Troubleshoot production issues spanning camera feeds, inference services, tracking behavior, and device constraints.

Leadership

 * Mentor CV engineers and collaborate cross-functionally (embedded, backend, product, ops).
 * Set standards for code quality, experimentation hygiene, reproducibility, and production readiness.


## Related Videos

- [Computer Vision from the Edge to the Cloud done easy](https://www.wearedevelopers.com/videos/263-computer-vision-from-the-edge-to-the-cloud-done-easy)
- [Deepfakes in Realtime - How Neural Networks Are Changing Our World](https://www.wearedevelopers.com/videos/180-deepfakes-in-realtime-how-neural-networks-are-changing-our-world)
- [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker)
- [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94)
- [Unboxing the DeepFace](https://www.wearedevelopers.com/videos/335-unboxing-the-deepface)
- [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker)

## Related Articles

- [6 Emerging Technologies We’ll Learn About in 2025](https://www.wearedevelopers.com/magazine/381-6-emerging-technologies-we-ll-learn-about-in-2025)
- [Dev Digest 112 - The True Crime of AI Development](https://www.wearedevelopers.com/magazine/421-dev-digest-112-the-true-crime-of-ai-development)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)
- [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)
- [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)
- [How software is steering vehicle technology](https://www.wearedevelopers.com/magazine/515-how-software-is-steering-vehicle-technology)