Machine Learning Engineer
London, England, United Kingdom
Hybrid / WFH Options
Hybrid / WFH Options
PhysicsX
methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings. Experience in ML/Computational Statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged A track record of scoping and delivering projects in a customer facing … and best practices (e.g., versioning, testing, CI/CD, API design, MLOps) Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow) Distributed computing frameworks (e.g., Spark, Dask) Cloud platforms (e.g., AWS, Azure, GCP) and HP computing Containerization and orchestration (Docker, Kubernetes) Strong problem-solving skills and the ability to analyse issues More ❯
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