4 of 4 Remote Permanent ONNX Jobs

Newcastle. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version More ❯

RF principles (RSSI, phase, multipath, filtering, spectral analysis, DSP) * Experience with sensor fusion and time-series learning (LSTM, Transformers, probabilistic filters). * Familiarity with deployment on constrained hardware (Jetson, ONNX Runtime, TensorRT). *Education and Experience* * BS required, MS preferred in Computer Science, Electrical Engineering, or related field. * 2+ years of hands-on experience developing and deploying ML models for More ❯

functionality. Within these tasks, you will have the opportunity to develop skills with the following tools and platforms: Matlab and Simulink Python Deep Learning libraries (e.g. Pytorch, scikit-learn, ONNX) C/C++ Hardware development boards (e.g. NVIDIA Jetson products) Alongside involvement with departmental activities, you will be part of a wider cohort of summer placements and graduates on the More ❯

using modern frameworks and libraries. Collaborate with data scientists, engineers, and stakeholders to translate business requirements into technical solutions. Optimize and deploy models using tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT. Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines. Work with large-scale data using PySpark and integrate models into production environments. Monitor model performance … Required Skills & Experience Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version … in a team. Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using PySpark. Solid understanding of software engineering principles and version More ❯