Embedded AI Engineer

We are seeking an experienced Machine Learning Engineer to build and deploy efficient ML models for real-time inference on edge devices, and to design systems for continuous model improvement through human-in-the-loop feedback.

Key Responsibilities

Model Development

• Develop and train efficient models for real-time detection/classification
• Iterate on architectures with a focus on robustness in real-world conditions

Edge Deployment & Optimization

• Design, implement, and deploy ML models on edge hardware
• Optimize models for latency, memory, and power usage
• Apply and benchmark compression techniques (quantization, pruning, distillation)

MLOps & Benchmarking

• Build automated pipelines for training, evaluation, and deployment
• Benchmark model size, latency, and accuracy across edge platforms (TFLite, TensorRT, ONNX Runtime)
• Monitor model performance and drift in production

Required Skills and Experience

• Experience deploying ML models on edge devices
• Strong understanding of model optimization (quantization, pruning, distillation)
• Solid experience with data-centric ML workflows (data collection, labeling, retraining)
• Proficiency in PyTorch and/or TensorFlow, and edge runtimes (TFLite, TensorRT, ONNX)
• Experience building ML systems end-to-end (data → training → deployment → monitoring)
• Strong analytical skills to manage trade-offs between accuracy, latency, and model size

Location

Denmark (Copenhagen) or Poland (Wrocław / Poznań)

About

We are a small, senior team working on a technology-driven project in an international context, focused on building reliable, real-world software systems. The work is hands-on and execution-driven, with the team involved end-to-end from architecture and prototyping to testing and deployment.