Q.ai Tech Stack

On-device AI framework for embedded hardware and consumer electronics

Technology, Information and Media Ramat Gan 11–50 employees Privately Held

Q.ai builds machine learning infrastructure optimized for embedded and low-resource devices. The tech stack reveals a company deep in edge AI: PyTorch, TensorFlow, and TensorFlow Lite paired with hardware design tools (SolidWorks, Rhino, Blender) and manufacturing-grade DevOps (Kubernetes, Terraform, Ansible, Chef). Active projects center on on-device model optimization, inference speed, and a next-generation consumer electronics device—suggesting Q.ai is shipping physical hardware, not just software.

Tech Stack 63 technologies

Core StackJenkins GitLab CI/CD Kubernetes Docker Python Go AWS Terraform CloudFormation Prometheus Grafana Ansible Puppet PyTorch TensorFlow MATLAB SolidWorks Bash Azure GCP Elasticsearch, Logstash, Kibana Chef TensorFlow Lite Wireshark LabVIEW Bluetooth Wi-Fi Rhino Blender KeyShot+33 more

What Q.ai Is Building

◆Challenges

Optimizing models for embedded devices
Reducing computational requirements
Improving inference speed
Maintaining high-performance ci/cd pipelines
Complex devops automation challenges
Improving devops workflow efficiency
Low resource compute constraints
Running models on low resource compute
Scalable manufacturing process
Assembly line integration

▲Active Projects

On-device machine learning framework advancement
On-device ai performance optimization
Ci/cd pipeline optimization
Cloud infrastructure improvement
Automation solution design
Modeling human communication methods
Metrics evaluation for models
Low resource compute model scaling
Next-generation consumer electronic device
Rapid prototyping and algorithm validation

Hiring Activity

Minimal20 roles · 0 in 30d

Department

Engineering

Data

Design

Ops

Seniority

Senior

Mid

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About Q.ai

Q.ai is an Israeli AI hardware and software company building machine learning frameworks optimized for on-device inference. The organization is engineering-heavy (16 of 20 active hires are engineers, mostly senior-level) with supporting data and design roles. Their tech stack spans model training (PyTorch, TensorFlow), embedded deployment (TensorFlow Lite), infrastructure automation (Kubernetes, Terraform, Ansible), and physical product design (SolidWorks, Rhino). Active projects include low-resource model scaling, rapid prototyping, and assembly-line integration, indicating they are developing a consumer electronics product alongside the underlying ML framework.

HeadquartersRamat Gan

Company Size11–50 employees

Hiring MarketsIsrael, United States

Frequently Asked Questions

What is Q.ai building?

An on-device AI framework for embedded hardware, focused on optimizing model inference for low-resource compute environments. Current projects include next-generation consumer electronics, model optimization for embedded devices, and manufacturing integration.

What tech stack does Q.ai use?

PyTorch, TensorFlow, TensorFlow Lite for ML; Kubernetes, Docker, Terraform, Ansible for infrastructure; SolidWorks, Rhino, Blender for hardware design; and GitLab CI/CD, Jenkins for deployment automation across AWS, Azure, and GCP.

Where is Q.ai hiring?

Primarily in Israel and the United States. Current open roles focus on senior engineers (18 of 20 active positions are senior-level), with smaller hiring in data, design, and operations.