51 to 75 of 465 PyTorch Jobs in the UK

. Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization More ❯

tooling: Git, Unix/Linux, Docker. Plus, a strong opinion on your IDE/editor of choice is welcome Familiarity with modern machine learning tools, for instance TensorFlow, Keras, PyTorch or SKLearn. Commercial experience with these is not essential. Excellent communication skills; both in customer-facing and internal team communication. Knowledge of MLOps is not essential, but some awareness of More ❯

and GenAI technologies for specific scenarios, estimate the time, cost and outcomes of different solutions, and apply effective risk management to ensure prompt delivery Deep experience in Python and PyTorch/TensorFlow. Industry experience in building scalable, production-grade AI services and automated data pipelines with tools like Docker, Kubernetes, and cloud platforms (e.g., AWS). Proven ability to collaborate More ❯

NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow, JAX, or similar). Demonstrated ability to work independently on real-world projects. Strong communicator, with the ability to share ideas, take feedback, and collaborate effectively. A master’s More ❯

NumPy, scikit-learn, etc.). Comfort working with large and complex datasets, including data wrangling and exploratory analysis. Experience (or strong interest) in GPU-powered model development (e.g. using PyTorch, TensorFlow, JAX, or similar). Demonstrated ability to work independently on real-world projects. Strong communicator, with the ability to share ideas, take feedback, and collaborate effectively. A master’s More ❯

learning or engineering roles. Strong expertise in Machine Learning and Deep Learning, with exposure to Reinforcement Learning as a plus. Proficiency in Python and modern ML libraries (e.g., TensorFlow, PyTorch, JAX, or Keras). Experience with version control systems (GitHub, GitLab) and knowledge of clean, maintainable coding practices. Familiarity with CI/CD pipelines for automating ML workflows. Ability to More ❯

natural language processing and computer vision) in industry. Master, PhD degree or equivalent experience in applied mathematics, computer science, bioinformatics or related scientific fields. Proficiency in software engineering (Python, Pytorch, JAX, Docker, Linux). A willingness to learn and develop skills in computational genomics including building an understanding of biological concepts related to cancer. Excellent communication skills in English. Appropriate More ❯

across departments and navigate large organisations. Strong project management and organizational skills. Proficiency in programming languages such as Python, Java and Scala, and experience with ML frameworks like TensorFlow, PyTorch, and scikit-learn. Experience with cloud platforms (e.g., AWS), big data technologies (e.g., Spark) as well as other technologies used to deploy models to production (e.g., Kubernetes, GHA, Airflow, Docker More ❯

we are looking for: Essential: UK SC Clearance or the ability obtain it Demonstrable experience developing a variety of machine learning models Strong grasp of machine learning frameworks (e.g. PyTorch, Tensorflow) Knowledge of machine learning architectures, loss functions, tools and techniques Experience training machine learning models, including hyperparameter tuning and optimizing model performance Experience with (or at least exposure to More ❯

learning, computer science, engineering, or a related discipline, with a BSc required and an MSc considered advantageous. Experienced in using machine learning frameworks such as Scikit-learn, Keras, and PyTorch, with additional familiarity with MLFlow and AzureML seen as a positive. Have working knowledge of CI/CD practices, ML Ops, ML pipelines, automated testing, and platforms such as AzureML More ❯

building something new. You should have: 2+ years of experience in machine learning or data science roles Strong programming skills in Python and experience with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) Solid understanding of machine learning algorithms and statistical methods Experience working with cloud platforms (AWS, GCP, or Azure) Familiarity with MLOps tools and practices is a plus A More ❯

technical abilities e.g. Data Engineering, Machine Learning, Cloud, DevOps & Software Good theoretical understanding of machine learning, neural networks, and other computer vision topics Hands-on experience with Python and PyTorch Hands-on experience with MLOps frameworks and platforms (We use ClearML!) Experience with additional programming languages and frameworks is a plus Experience working with cloud platforms (GCP, AWS, Azure) Experience More ❯

Strong Statistical Analysis & Data Evaluation, youre comfortable developing or learning to develop custom metrics, identify biases, and quantify data quality. Strong Python skills for Data & Machine Learning, familiarity with PyTorch and TensorFlow. Experience with distributed computing and big data scaling ML pipelines for large datasets. Familiarity with cloud-based deployment (such AWS, GCP, Azure, or Modal). Experience in fast More ❯

Claude, Gemini, Llama, Falcon, Mistral. Model performance and optimization: Fine-tuning and optimizing LLMs for quality, latency, sustainability, and cost. Programming and NLP tools: Advanced Python, frameworks like PyTorch, TensorFlow, Hugging Face, LangChain. MLOps and deployment: Docker, Kubernetes, Azure ML Studio, MLFlow. Cloud and AI infrastructure: Experience with Azure Cloud for scalable deployment. Databases and data platforms: SQL, NoSQL, Snowflake More ❯

and delivery What we're looking for 6+ years of experience in software engineering with strong focus on machine learning systems Deep proficiency in Python and ML ecosystem (e.g. PyTorch, scikit-learn, MLFlow) Solid understanding of data and model lifecycle management, versioning, and deployment Experience building ML infrastructure and model-serving pipelines in production environments Familiarity with cloud-based architecture More ❯

rapidly evolving AI landscape, identifying and adopting new techniques, tools, and methodologies as appropriate. Requirements: Excellent programming ability in Python and good experience with machine learning libraries such as PyTorch (preferred), TensorFlow, OpenCV etc. Experience in deploying, maintaining, and optimising deep learning pipelines, focusing on efficiency, performance, and production maturity. Strong understanding of machine learning principles, deep learning techniques and More ❯

with a strong focus on AI and machine learning. Solid understanding of machine learning algorithms, data structures, and statistical modelling. Experience with AI frameworks and libraries such as TensorFlow, PyTorch, or Scikit-learn. Strong programming skills in languages such as Python, Java, or Scala. Strong experience with cloud-based AI services and platforms (e.g., AWS, Azure, GCP, ServiceNow, SalesForce). More ❯

as Python, R, C, C++, or Java, and their associated security considerations. * Previous experience with ML, LLM, deep learning and data manipulation techniques, libraries, and frameworks such as TensorFlow, PyTorch, Jax, and scikit-learn is desirable. * Experience in implementing secure coding practices, DevOps, CI/CD pipelines and familiarity with secure software development life cycle (SDLC) methodologies. * Strong understanding of More ❯

and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, ResNets, VAEs, GANs, Markov chains, etc. Experience using specialized machine learning libraries eg. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of using Cloud technologies More ❯

responses and cancelable endpoints to support real-time and interactive use cases. Preferred skills and experience Machine learning experience : Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn. Natural Language Processing (NLP) : Experience with NLP techniques and tools, such as spaCy or NLTK. Distributed systems : Knowledge of distributed systems and experience with tools like Kubernetes More ❯

data science or software engineering. An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

programming skills in languages such as Python with a strong focus on code optimization, modular design, and efficiency. Deep expertise in developing, training, and deploying models using frameworks like PyTorch and TensorFlow. Demonstrated experience in building scalable, production-grade AI services and automated data pipelines with tools like Docker, Kubernetes, and cloud platforms (e.g., AWS). Strong understanding of data More ❯

programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

to clearly communicate and present to internal and external stakeholders. Nice to have, but not essential NLP/Deep learning experience (e.g. huggingface, spaCy) Deep learning framework experience (preferably PyTorch) MLOps experience (e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Cloud platform experience, especially from an ML standpoint (AWS preferred) Statistical testing experience Experience with More ❯