12 of 12 PyTorch Jobs in Central London

scalable systems. Essential experience includes: Commercial experience developing and deploying machine learning models in Python Proficiency with ML frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow Strong experience delivering ML solutions using AWS services (e.g. SageMaker, Lambda, S3) Expertise in experiment design, including hypothesis formulation, A/B testing ...

technical leadership across the team. What We're Looking For Strong commercial experience developing machine learning solutions using Python. Experience with frameworks such as: PyTorch TensorFlow Scikit-learn XGBoost Experience deploying ML solutions on AWS, including services such as SageMaker, Lambda, and S3. Strong understanding of MLOps tooling, including MLflow ...

experience in managing, coaching and developing junior members of a team and wider community. D ESIRABLE : Demonstrable experience with modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage ...

technical leadership level, leading engineering teams and influencing senior stakeholders. Strong hands-on experience with Python and modern AI/ML frameworks such as PyTorch and/or TensorFlow . Deep understanding of LLMs, Generative AI, prompt engineering, RAG architectures and vector databases . Experience with frameworks and tooling such ...

cloud environment. PREFERRED QUALIFICATION Proficiency in Python and python-based AI/ML framework and familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch).. Experience working with language models like LLM's APIs and optimizing their usage for specific applications. Experience with the following programming languages: Python, C++, Java ...

computer vision, audio, graphs, time-series or tabular data Strong Python skills and experience with common libraries (e.g. pandas, NumPy, scikit-learn, PyTorch, Transformers, statsmodels, PyMC) Understanding of software engineering and ML best practices, including version control, packaging and model deployment Experience training and deploying models using cloud platforms, particularly ...

modern software engineering practices Machine Learning/Deep Learning MLOps and production ML systems Cloud platforms and scalable infrastructure ML frameworks such as PyTorch, TensorFlow or equivalent LLMs/Generative AI is highly desirable CI/CD, Docker, Kubernetes or similar technologies Designing and operating production-scale ML platforms ...

AIOps infrastructure and tooling. Proficient in problem-solving and analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX. Experience with Large Language Models, including RAG and fine-tuning techniques. Familiarity with compute infrastructure necessary to support operating AI and ML technology. ...

proficiency in GCP’s ML/AI tooling, and its data ecosystem in general. Expert proficiency with Python and its core ML libraries e.g. PyTorch, TensorFlow. Expert proficiency with SQL development, with Google dialects being advantageous. Robust CI/CD discipline for ML, for example, GitHub Actions or GitLab ...

theory We're looking for: Strong hands-on AI/ML and Data Science expertise (Python essential — NumPy, Pandas, Scikit-learn; exposure to PyTorch, TensorFlow, LangGraph or similar a plus) Proven experience delivering technical training to a mix of technical and non-technical audiences, virtually and face-to-face Experience ...

related quantitative field. Expert proficiency in GCP’s ML/AI tooling, its data ecosystem, and Python with core ML/AI libraries (PyTorch, XGBoost, TensorFlow). Expert proficiency with SQL development, with Google dialects being advantageous. Proficiency with an API web framework (FastAPI, Django, Flask). Robust CI/ ...

model serving infrastructure or inference backends. Preferred qualifications: Experience with developing serving infrastructure. Experience programming hardware accelerators (GPUs, TPUs) via ML frameworks (e.g., JAX, PyTorch) or low-level programming models (e.g., Pallas, CUDA, OpenCL). Experience profiling software to identify performance bottlenecks. Experience with distributed ML systems optimization and parallelism ...