Smart Kitchen ML Engineer (Edge & Vision)

Chefman; Mahwah (NJ); On-site; USD 140,000 - 170,000; Full time

Job summary

CHEF iQ is hiring a Machine Learning Engineer to own the strategy, research, development, and deployment of AI capabilities powering next-generation kitchen products.

You will design, train, and deploy ML and computer vision models, build datasets, and manage the full lifecycle from data prep to monitoring. Collaboration with firmware, software, and hardware teams is required to deliver autonomous cooking experiences.

Qualifications

• Experience developing and deploying machine learning models in production environments.
• Strong background in computer vision, image classification, object detection, deep learning, or related ML apps.
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow.

Responsibilities

• Design, train, and deploy machine learning and computer vision models powering autonomous cooking experiences.
• Develop image classification, object detection, and state-recognition models for cooking inputs.
• Build and manage datasets including labeling, prep, augmentation, and validation.
• Own the full ML lifecycle from data prep to deployment, monitoring, and improvement.
• Research and apply emerging ML techniques to improve product performance and user experience.
• Deploy and optimize models for cloud and edge devices, balancing accuracy and latency.
• Collaborate with firmware, software, hardware, and product teams to integrate ML capabilities.
• Develop AI-driven systems combining vision, sensor, and contextual data for intelligent recommendations.

Skills

Machine learning

Python

PyTorch

TensorFlow

Embedded systems

AWS

GCP

Azure

MLOps

Computer vision

Debugging

Tools

TensorRT

ONNX Runtime

OpenVINO

TensorFlow Lite

Job description

CHEF iQ is hiring a Machine Learning Engineer to own the strategy, research, development, and deployment of AI capabilities powering next-generation kitchen products.

You will design, train, and deploy ML and computer vision models, build datasets, and manage the full lifecycle from data prep to monitoring. Collaboration with firmware, software, and hardware teams is required to deliver autonomous cooking experiences.