Machine Learning Engineer

Chefman•Mahwah, NJ

3h•$140,000 - $170,000

About The Position

In 2020, we launched CHEF iQ, an ecosystem of connected kitchen appliances designed to transform how people cook and connect through food. Our mission is to make great cooking effortless through intelligent technology, guided experiences, and seamless integration between hardware, software, and AI. As a Machine Learning Engineer, you will play a critical role in shaping the future of cooking. Working on a small, high-impact team, you will have significant ownership over the strategy, research, development, and deployment of AI capabilities that power next-generation kitchen products. From computer vision models that understand what is happening inside an oven to embedded AI systems that make real-time cooking decisions, you will help define how machine learning is applied within consumer appliances. This is an opportunity to work at the intersection of machine learning, embedded systems, computer vision, and smart consumer technology, bringing cutting-edge AI from research into products used by millions of home cooks.

Requirements

Experience developing and deploying machine learning models in production environments.
Strong experience with computer vision, image classification, object detection, deep learning, or related machine learning applications.
Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, or similar technologies.
Experience building and managing datasets used for machine learning model development.
Experience deploying or optimizing models for embedded systems, edge devices, or resource-constrained environments.
Experience working with public cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure, including their machine learning and AI services.
Experience with multimodal foundation models, vision-language models (VLMs), or other AI systems that combine vision, language, and contextual understanding.
Experience with MLOps practices including model lifecycle management, experiment tracking, model monitoring, and CI/CD pipelines for machine learning systems.
Understanding of model optimization techniques such as quantization, pruning, and inference acceleration.
Ability to independently evaluate new technologies, research, and model architectures.
Strong analytical, problem-solving, and debugging skills.
Excellent communication and cross-functional collaboration skills.

Nice To Haves

Experience with embedded Linux, ARM-based platforms, or edge AI hardware.
Experience with TensorFlow Lite, ONNX Runtime, OpenVINO, TensorRT, or similar deployment frameworks.
Experience with connected consumer products, IoT devices, robotics, or embedded vision systems.
Experience with large language models (LLMs), small language models (SLMs), vision-language models (VLMs), generative AI, recommendation systems, agentic AI systems, or AI-powered user experiences.
Experience with retrieval-augmented generation (RAG), vector databases, embeddings, semantic search, or knowledge retrieval systems.
Experience designing AI agents capable of monitoring, planning, reasoning, and decision-making using vision, sensor, and contextual data.
Experience with AWS machine learning and AI services preferred.

Responsibilities

Design, train, and deploy machine learning and computer vision models that power autonomous cooking experiences within CHEF iQ products.
Develop image classification, object detection, and state-recognition models that identify food types, cooking progress, doneness levels, and other key inputs used to guide cooking decisions.
Build and manage datasets, including data collection, labeling, preparation, augmentation, and validation.
Own the full machine learning lifecycle, from data preparation and model training through deployment, monitoring, and continuous improvement.
Research, evaluate, and apply emerging machine learning techniques, including computer vision, generative AI, large language models (LLMs), vision-language models (VLMs), multimodal AI, and academic research, to improve product performance and customer experiences.
Deploy and optimize models for cloud and edge devices, balancing accuracy, latency, memory usage, power consumption, and overall system performance.
Collaborate with firmware, software, hardware, and product teams to integrate machine learning capabilities into consumer products.
Develop systems that combine vision, sensor, and contextual data to enable intelligent recommendations and autonomous next-step actions.
Design and develop AI-driven systems that combine perception, reasoning, and decision-making capabilities to enable intelligent and autonomous cooking experiences.
Establish testing methodologies and performance metrics to validate models across real-world usage scenarios.
Document model architectures, experiments, and deployment approaches.