Staff Machine Learning Engineer (Edge AI)

Location: Hybrid role based in Dublin, Ireland, with office attendance three days per week. Remote work is available for candidates located in other geographies.

Company

hirify.global develops software and production-grade AI technologies for intelligent software-defined vehicles, with solutions deployed across more than 8 million vehicles.

What you will do

• Build and train edge AI models, including Transformers, LLMs, CNNs, and LSTMs, to process application logs, kernel traces, and multimodal vehicle data.
• Develop supervised and unsupervised models for anomaly detection, log clustering, software regression detection, and failure prediction.
• Correlate CAN bus, sensor, system, and trace anomalies to identify root causes and crash precursors.
• Own the end-to-end ML pipeline from data ingestion and training through deployment on resource-constrained edge devices.
• Port and optimize PyTorch and TensorFlow models for CPUs, GPUs, and embedded NPUs using quantization, pruning, distillation, and memory optimization.
• Lead edge ML architecture, define on-device versus cloud processing strategy, and mentor junior engineers.

Requirements

• Bachelor’s degree in Computer Science, Electrical Engineering, Software Engineering, or a related field.
• 7+ years of Machine Learning Engineering experience, including 3+ years focused on Edge AI or embedded systems.
• Expert Python and working knowledge of modern C++14/17 for inference.
• Deep proficiency with PyTorch or TensorFlow and inference engines such as ONNX, TFLite, or TVM.
• Experience with NLP, sequence modeling, anomaly detection, sensor time-series data, and deployment on ARM-based or other resource-constrained edge environments.
• Experience leading technical projects from concept to production and mentoring junior engineers.

Nice to have

• Strong computer vision experience.
• MS or PhD in Computer Science, Engineering, or a related field.
• Automotive experience with CAN, DBC files, UDS, SOME/IP, or MQTT.
• Knowledge of Linux or QNX kernel logs, process states, and OS-level debugging.
• Experience with NVIDIA TensorRT or Qualcomm SNPE.

Culture & Benefits

• Fast-paced startup environment with direct impact on fleet reliability and vehicle software.
• Collaboration with vehicle software, systems, MLOps, and AI integration specialists.
• Opportunity to experiment with advanced model architectures and development tools.
• Work on AI-enabled software-defined vehicles deployed in production.