AI Engineer

Job DescriptionWe generate 92 million tons of textile waste every year of which we are burning more than 90% because of our inability to repurpose and recycle them effectively.At Cyclothe, we are redesigning the textile waste management and are looking for an AI Engineer to join our team.About us:Founded in 2023 in Stockholm, Cyclothe developsdisruptivetextile waste management technologies that champion a reuse-first approach and are built to meet the needs of a rapidly evolving circular economy.By creating tools that empower participation and enforce transparency across the value chain, we break the linear waste paradigm and accelerate the transition to a truly circular textile future.As we scale from pilots into production deployment, we are looking for an AI Engineer who will drive our intelligence layer: data, models, robotics, computer vision, edge-IoT and inference systems.Who are we looking for?We are redesigning the entire textile waste management ecosystem which starts with solving the problems of textile traceability, robotic sorting and effective redistribution.So, the primary responsibilities are to:Design, build and deploy machine learning / deep learning models for tasks like: textile material recognition, condition/quality scoring, multi-material detection, colour/fabric type classification, and robotics control.Work closely with hardware/robotics teams to integrate vision sensors (e.g., NIR, VIS, hyperspectral) and AI pipelines into automated sorting systems.Develop data-collection pipelines: ingesting imagery, sensor data, label workflows, annotation tools, synthetic data augmentation, continuous learning loops.Optimize edge/embedded inference (on-device, low latency) and/or cloud-based inference architectures for scalability across sorting units and deployment sites.Monitor, evaluate, and improve model performance (accuracy, throughput, robustness, drift detection) in real-world conditions, including dusty/wear-and-tear environments.Collaborate across product, operations, software/firmware and business teams: translate commercial requirements (e.g., "70% reduction in textile waste", "20% cost reduction per ton pre-sorted") into technical deliverables.Help build a data-driven backbone for traceability: linking each item drop, sensor readout, AI classification, and downstream reuse/recycle outcome in the Cyclothe platform.Skills & RequirementsStrong experience (3-5+ years) building production-ready machine learning / deep learning systems, ideally in computer vision (image classification, segmentation), sensor fusion, embedded/edge deployment.Experience with Python + ML frameworks (TensorFlow, PyTorch), computer vision libraries (OpenCV, etc), model optimization for embedded/edge execution (e.g., ONNX, TensorRT).Good understanding of data pipelines: image/sensor acquisition, annotation, training/validation/test splitting, deployment monitoring.Comfort working in lean startup environments, able to wear multiple hats and iterate quickly.Fluent in English; Swedish and Turkish is a plus.Preferred (nice-to-haves):Familiarity with near-infrared (NIR) / VIS spectroscopy, material recognition in recycling/waste context.Experience integrating ML/AI in robotics or automated sorting systems (hardware + software).Experience with cloud services + edge inference (AWS, Azure, GCP + on-device).Passion for sustainability, circular economy, and impact-driven tech.Tech stack / environment you will influence & operate inYou will have a key role in shaping Cyclothe's intelligence stack. Today our environment includes:Multi-camera industrial vision (RGB, VIS, NIR, hyperspectral – expanding)NVIDIA edge inference (Jetson AGX Orin family) + industrial roboticsOn-device real-time decisioning (