1 to 25 of 50 PyTorch Jobs in the Thames Valley

Required Skills & Experience Proven experience (3–5+ years) as a Machine Learning Engineer , Data Scientist , or similar role. Strong programming skills in Python (experience with libraries such as TensorFlow, PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker More ❯

What We’re Looking For: 🎓 A degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field 🐍 Proficiency in Python and familiarity with libraries such as TensorFlow, PyTorch, or scikit-learn 🧩 Understanding of machine learning algorithms and statistical modelling 📈 Strong problem-solving and analytical mindset 🗣 Excellent communication skills and a genuine passion for learning Nice to Have: 💾 Experience More ❯

collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary More ❯

experience in professional software development Strong Python skills and a track record of delivering maintainable, well-tested code Experience with LLM APIs (OpenAI, Hugging Face, Anthropic) or ML frameworks (PyTorch, TensorFlow) Solid understanding of CI/CD, Git, testing, and agile methodologies Hands-on experience with Linux, Docker, and containerized deployments Familiarity with data engineering concepts (SQL, ETL, data lakes More ❯

and AI Required Skills & Qualifications: Strong experience in C#, .NET Framework, .NET Core/6/7, ASP.NET Experience with AI/ML frameworks such as: ML.NET TensorFlow/PyTorch (for integration with .NET) Azure Cognitive Services/OpenAI/AI APIs Knowledge of RESTful APIs , microservices, and cloud platforms (Azure, AWS, or GCP) Familiarity with SQL and NoSQL databases More ❯

ML models into products. Contribute to improving infrastructure, tooling, and best practices for ML development and deployment. Skills & Experience Essential: Strong programming skills in Python (and frameworks such as PyTorch, TensorFlow, or Scikit-learn). Proven experience in developing and deploying machine learning models in production. Solid understanding of data structures, algorithms, and software engineering principles. Experience with ML pipelines More ❯

and understands the full ML lifecycle. Can design for long-term scalability, reliability, and resilience. Has strong programming skills with Python – essential. Has hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn). Is experienced with cloud platforms (ideally GCP: BigQuery, Vertex AI, Dataflow), but AWS/SageMaker or similar is also valued. Has operated in distributed computing environments More ❯

scripts, and building custom scenarios to prove a point. Highly Desired: Direct experience with our specific AI domain (e.g., experience with LangChain for an LLM company, or TensorFlow/PyTorch for a computer vision company). Understanding of MLOps principles and the end-to-end machine learning lifecycle. Programming/scripting experience (e.g., Python, SQL) to manipulate data and interact More ❯

in NLP, transformer models, deep learning, and scalable AI pipelines, with demonstrated involvement in LLM development, fine-tuning, or deployment. Technical Skills: Proficiency in Python and frameworks such as PyTorch , TensorFlow , or JAX . Strong familiarity with distributed computing and data engineering tools (e.g., SLURM , Apache Spark , Airflow ). Hands-on experience with LLM training, fine-tuning, and deployment (e.g. More ❯

as Code (IaC), and cloud provisioning (OCI, AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting More ❯

as Code (IaC), and cloud provisioning (OCI, AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting More ❯

as you grow What We’re Looking For: 🎓 A degree in Artificial Intelligence, Computer Science, Mathematics, or a related field 🐍 Proficiency in Python and experience with frameworks such as PyTorch , NumPy , or Scikit-learn 🧩 Understanding of machine learning algorithms, optimisation, and model evaluation 📈 Strong mathematical reasoning and analytical problem-solving skills 🗣 Excellent communication skills and a genuine passion for learning More ❯

data processing and training models in a distributed environment. LLMs: Understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Proficiency in Python using PyTorch/TensorFlow/JAX framework. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g., AWS, GCP or Azure), an understanding of containerisation (e.g., Docker). Algorithms and More ❯

years in a leadership role. Proven experience delivering production-grade systems in Search, Recommender Systems , or Personalisation . Strong understanding of ML fundamentals and deep learning frameworks (e.g., PyTorch, TensorFlow, JAX). Hands-on experience with GenAI , including working with LLMs (e.g., OpenAI, Anthropic, HuggingFace models). Deep knowledge of MLOps, experimentation, and model evaluation techniques. Experience scaling ML platforms More ❯

and training models in a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms More ❯

and training models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms More ❯

quantitative libraries and shared research infrastructure. Qualifications & Skills Essential: Strong expertise in machine learning, statistical modelling, and numerical methods with practical applications. Proficiency in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow) and experience with C++ or Java for high-performance model integration. Solid understanding of Fixed Income products, yield curve modelling, and financial mathematics. Experience building production-level ML More ❯

learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with marketing, CRM More ❯

communicator across technical teams and stakeholders Effective problem-solver who takes initiative in complex production settings Experience with scientific computing, deep learning, big data, or health IT ontologies (e.g., PyTorch, JAX, Spark, HL7, FHIR) (desirable) Familiarity with cloud infrastructure (Azure/AWS), infrastructure as code, Kubernetes, Linux, Docker, data pipelines, and MLOps tools (desirable) Passion for biomedical topics and startup More ❯

communicator across technical teams and stakeholders Effective problem-solver who takes initiative in complex production settings Experience with scientific computing, deep learning, big data, or health IT ontologies (e.g., PyTorch, JAX, Spark, HL7, FHIR) (desirable) Familiarity with cloud infrastructure (Azure/AWS), infrastructure as code, Kubernetes, Linux, Docker, data pipelines, and MLOps tools (desirable) Passion for biomedical topics and startup More ❯

and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights for coaches More ❯

for reinforcement learning or transformer based architectures • Hands on experience building and maintaining Frontend applications - React, Typescript • Strong programming background in Python and or relevant frameworks such as TensorFlow, PyTorch or Hugging Face • Fluent in cloud native development, containerisation, orchestration (for example Docker or Kubernetes) and CI/CD • Exceptional problem solver with an entrepreneurial mindset who is comfortable with More ❯

in Reinforcement Learning (policy optimisation, reward modelling, RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs/TPUs. If this More ❯

data modeling Containers: Docker; orchestration tools Infra as Code: Terraform, CloudFormation Monitoring: Prometheus, Grafana, ELK Security & Compliance: Data protection and access control Nice-to-Haves AI/ML integration (PyTorch, HF etc.) MLOps (MLflow, Kubeflow, SageMaker) Vector search (Pinecone, pgvector, Weaviate, Qdrant etc.) Video ingestion pipelines & codecs 💡Don’t worry if you haven’t used these exact tools. If you More ❯

consultancy, or fast-paced business. Strong programming skills in Python, with experience building automation workflows, APIs, and data pipelines. Exposure to AI/ML frameworks (e.g. OpenAI, LangChain, TensorFlow, PyTorch). Solid knowledge of SQL and data warehousing concepts. Demonstrated experience with cloud platforms ( GCP & Azure preferred). Strong understanding of data modelling, transformation, and orchestration (e.g. dbt, Airflow, Azure More ❯