5 of 5 Remote/Hybrid TensorFlow Jobs in Oxford

Core Technical Skills 3+ years of experience in Machine Learning, Data Science, or related roles. Strong programming skills in Python. Experience with ML frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost. Solid understanding of ML algorithms, statistics, and evaluation metrics. Experience deploying ML models into production environments. Data & Engineering Skills Strong ...

maintainable code Solid understanding of ML fundamentals. For example feature engineering, model selection, handling imbalanced data, choosing and interpreting metrics Experience with PyTorch or TensorFlow GenAI specific Hands on experience with LLM APIs or open source models such as Llama or Mistral Experience building RAG systems with vector databases ...

STEM field. Strong foundation in machine learning concepts, algorithms, and model development. Experience with Python and core ML libraries (e.g., NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch). Understanding of software engineering principles and version control (Git). Familiarity with cloud platforms (AWS, GCP, or Azure) is a plus. Excellent ...

experience deploying LLMs, transformers, or other neural networks Proven experience with prompt engineering techniques and LLM optimization Experience with ML frameworks (PyTorch/TensorFlow) and model serving Proficiency with relational databases and AWS (PostgreSQL/MySQL) AWS experience on basic services (ECS, S3, etc.) Experience with containerization (Docker ...

regulated environment with sensitive data. You'll work with engineering to move models from idea to production. Tech Stack: Python, scikit-learn, PyTorch, TensorFlow/Keras, LangChain, Pydantic, Spark, Kafka, Databricks, MLflow, Azure, Docker, Kubernetes. Salary: Up to £100k Working Environment: Fully Remote in UK What … Research, prototype, and deploy LLM use cases (Q&A, summarisation, document processing) Build and tune models using scikit-learn, PyTorch, TensorFlow/Keras, and XGBoost Create data pipelines for batch and streaming with Spark and Kafka Use Databricks and MLflow for experiments, deployment, and monitoring What you'll bring ...