7 of 7 ONNX Jobs in the UK excluding London

MLflow, Weights & Biases). Data wrangling and analysis (Pandas, NumPy, SQL); familiarity with Spark or similar is a plus. Model optimisation and deployment fundamentals: ONNX, TorchScript, FastAPI/gRPC; GPU acceleration (CUDA basics desirable). Responsible AI and security awareness: explainability, privacy‐preserving methods (e.g., differential privacy, federated learning), adversarial ...

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 ...

collaborate closely with FPGA engineers on data-path integration (Vitis/Vivado a bonus). Model deployment and optimisation for edge devices using ONNX Runtime, TensorRT, TVM or similar frameworks. Solid engineering practice across Git, CI/CD, documentation, debugging and profiling. UI and visualisation experience using Qt, React, Electron ...

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 ...

shared training systems Preparing models for efficient on-device execution, balancing performance with mobile hardware limitations Deploy models for edge inference using CoreML and ONNX, optimising for mobile device constraints Working closely with mobile engineering teams to embed AI functionality into production SDKs Defining and improving methods for evaluating, benchmarking … approaches Research background or publications in relevant areas Tech stack • ML/AI: PyTorch, MLflow, SageMaker, ZenML • Infrastructure: AWS, Kubernetes, Docker • Edge deployment: CoreML, ONNX • Data: Python, S3, multimodal sensor data pipelines • Collaboration: JIRA, Git, structured OKR methodology Why you will enjoy working with our client: You’ll join ...

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 … 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 ...

learning models through quantisation, pruning, and fine-tuning – Evaluating architectures for speed/accuracy trade-offs across devices – Exporting models for runtime use (e.g. ONNX, TorchScript) – Supporting research-to-deployment workflows that accelerate iteration – Helping define a scalable ML pipeline for edge and embedded use You'll bring: – Proven experience …/ML model optimisation and deployment – Knowledge of TensorRT, ONNX Runtime or similar inference runtimes – Hands-on experience with CUDA, low-latency profiling and C++ integration – Strong command of model export tools and deployment best practices – A performance-first mindset and confidence in debugging at system level Why them? – Strong ...