Machine Learning (ML) Engineer - Applied

ModelCat | Remote from Europe About ModelCat 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 and expanding into new data domains Excellent problem-solving skills and strong written and verbal English communication Preferred Experience with cloud platforms (AWS) and web technologies (Node.js, REST APIs) Familiarity with compute cluster tools such as Ray and Optuna Knowledge of model compression techniques: pruning, quantization, transfer learning, knowledge distillation Experience defining software architecture for ML systems Familiarity with CI/CD practices Understanding of embedded systems concepts Experience with non-ML algorithms and signal processing Mindset Proactive, entrepreneurial approach — you thrive with ownership and ambiguity Startup mentality: you move fast, learn faster, and care deeply about the outcome Why Join ModelCat Market Opportunity — Edge AI is exploding, and we're solving a critical pain point in a massive and growing market Real Customer Impact — Our platform compresses 12–24 months of model development into 24–48 hours — validated by customers like NXP and Silicon Labs Technical Depth