Machine Learning (ML) Engineer - Applied

ModelCat is transforming how companies develop AI models for embedded, edge, and IoT devices. Their platform simplifies the AI development process, reducing it from 12–24 months to 24–48 hours, enabling developers, data scientists, and product owners to build AI solutions efficiently.

Location: Remote from Europe

Responsibilities

• Contribute to the development and enhancement of AutoML system for Edge AI, including pipelines that combine deep-learning and conventional algorithms for embedded devices
• Build and improve platform features across compute clusters and web applications
• Define abstractions and contribute to the architecture of cloud, cluster, and embedded components
• Integrate new ML use-cases across data domains including time-series, audio, object re-identification, segmentation, keypoints, action recognition, radar and point cloud data, multi-modal data, small language models, classification, and object detection
• Work with foundational computer vision and non-CV ML models — train, evaluate, modify, and combine them

Requirements

• 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

Benefits

• 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 — Work on hard, meaningful problems at the intersection of AutoML, TinyML, and embedded systems
• Growth Trajectory — Join during a pivotal growth phase with significant room to grow into technical leadership
• Competitive Compensation — Base salary, performance-based bonus, and meaningful equity stake