4 of 4 Permanent ONNX Jobs in the UK

cross-functional teams to address engineering challenges and deliver optimized solutions. Required Skills & Experience ML Frameworks : Expert in TensorFlow (TF2/Keras), experience with ONNX Runtime for inference. Programming : Advanced Python for ML; strong understanding of packaging, type checking, and performance profiling. Architecture : Proven experience designing scalable ML systems ...

frameworks such as TensorFlow, Keras, or PyTorch. Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost. Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe). Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or Azure ML Pipelines. Experience working with distributed data processing (e.g. ...

e.g., text-to-3D, video generation). Knowledge of NeRFs, Gaussian splatting, or neural scene representations. Experience optimizing models for real-time inference (TensorRT, ONNX, quantization, distillation). GPU programming experience (CUDA, Triton, or similar). Track record of publications at top-tier venues (NeurIPS, CVPR, ICML, ICLR, SIGGRAPH, ECCV ...

rate (FAR) and false rejection rate (FRR) at production-grade thresholds. Cross-platform deployment: Convert and optimise models for CoreML (iOS), TFLite (Android), and ONNX Runtime or equivalent (Desktop). Manage the differences in inference performance across runtimes. Benchmarking: Establish the performance benchmarking pipeline. Verification speed, battery impact … 15fps. Qualifications Tools & proficiency ML Frameworks: PyTorch (expert — primary training), TFLite (expert — Android/iOS inference), CoreML (strong — iOS Neural Engine), ONNX (strong — cross-platform interop), MediaPipe (competent — Android face landmarker integration). Computer Vision & Signal Processing: OpenCV (expert), NumPy/SciPy (expert — rPPG signal processing), dlib (competent), rPPG methods ...