Machine Learning (ML) Engineer - Applied

## About the company ModelCat is transforming how companies develop AI models for embedded, edge, and IoT devices. Our innovative platform uses AI to build AI — turning model architecture selection, training, optimization, and validation into a single powerful step. ModelCat takes what was previously a 12–24 month process requiring highly skilled AI professionals and reduces it to a 24–48 hour AI-powered job that can be run by developers, data scientists, and product owners. Trusted by industry leaders like NXP and Silicon Labs, ModelCat is a venture-backed startup headquartered in Sunnyvale, California. ## The Role We're seeking a motivated ML Engineer to help advance our AutoML platform. You'll play a key role in expanding its capabilities, onboarding new ML use-cases across vision, time-series, and beyond, and improving the product as we scale. This role offers meaningful growth potential toward a technical leadership track. ## What You'll Do ### AutoML Platform Development - Contribute to the development and enhancement of our AutoML system for Edge AI, including pipelines that combine deep-learning and conventional algorithms for embedded devices - Object tracking, multi-model pipelines, and emerging use-cases - Build and improve platform features across compute clusters and our web application - Define abstractions and contribute to the architecture of cloud, cluster, and embedded components ### ML Use-Case Expansion - Integrate new ML use-cases across a broad range of data domains and maintain and improve existing ones, including: - Time-series and audio, object re-identification, segmentation and keypoints - Action recognition (video), radar and point cloud data, multi-modal (vision + audio + sensor) - Small language models (NLP/SLM), classification, and object detection - Work with foundational computer vision and non-CV ML models — train, evaluate, modify, and combine them to unlock new functionality ### Edge AI Optimization & Deployment - Optimize AI solutions for edge devices using TinyML frameworks, creating models that fit a range of chip sizes and memory constraints - Deploy ML and non-ML algorithms on embedded targets (MCU and application-class microprocessors) - Productize research-quality code into robust, production-ready systems ### Collaboration & Craft - Partner on data strategies, preprocessing pipelines, and model training workflows - Stay current with Edge AI and AutoML advancements - Document your work and contribute to technical reports ## Who You Are ### Required - Master's degree in CS, EE, or a related field (PhD a plus) - 4+ years of relevant industry experience in ML (AutoML and Edge AI experience highly valued) - Strong Python skills with the ability to write production-quality code; C/C++ a plus - Solid command of ML frameworks: TensorFlow, PyTorch, ONNX - Proficient with the standard DS toolset: scikit-learn, OpenCV, pandas - Comfortable working in Linux-based development environments - Experience onboarding new ML use-cases