1 to 25 of 123 PyTorch Jobs in Central London

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 ❯

the language) and in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modeling workflows. ML Ops & deployment More ❯

Science, Engineering, Mathematics, or a related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI More ❯

for sensitive environments. Collaborate across engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3 More ❯

Excellent customer-facing communication across scientific and engineering audiences. Experience in pharma, biotech, medtech, or healthcare environments (ideal). MSc in Computer Science or equivalent experience. Bonus Familiarity with PyTorch, JAX, Hugging Face, and AWS ML services. Experience optimizing models for low-latency inference. Open-source contributions and/or startup experience. Why This Is Unique Globally competitive salary + More ❯

RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools such as Terraform . Strong background in technology deployment, including More ❯

/LLM/NLP/CV models. - Strong understanding of machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit-learn). - Experience with modern AI concepts: prompt engineering, embeddings, vector search, etc. - Skilled in managing complex codebases (Git) and working with cloud platforms (GCP, AWS). - Excellent More ❯

Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage data pipelines for analytics and modelling workflows. Model development & deployment More ❯

Skills: Proficiency in Python, with a deep understanding of machine learning algorithms, deep learning, and generative models. Advanced AI Skills and Testing: Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), hands-on experience in deploying AI/ML solutions as a service/REST API on Cloud or Kubernetes, and proficiency in testing of developed AI components. Responsibilities also include More ❯

. Deep expertise in supervised/unsupervised learning, time series forecasting, deep learning, and generative AI. Strong programming skills in Python and SQL; experience with ML frameworks (e.g., TensorFlow, PyTorch) and cloud platforms (AWS, Azure, GCP). Commercially minded, with the ability to connect technical solutions to business value. Experience in financial services, fintech, or trading environments is highly desirable. More ❯

production. What We’re Looking For 3+ years of experience in Data Science. Strong proficiency in Python and experience with ML libraries/frameworks (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and feature/data platforms More ❯

involvement in LLM projects including training, fine-tuning, or production deployment Strong track record in scalable AI system development Technical Expertise Expert-level Python programming with deep knowledge of PyTorch, TensorFlow, or JAX Proven experience with distributed computing environments and modern MLOps workflows Practical knowledge of LLM toolkits (Hugging Face ecosystem, training frameworks) Strong problem-solving abilities for complex model More ❯

contribute to the broader ML/NLP community Technical Skills: Degree in Computer Science, Engineering, Bioinformatics, or related field (BSc, MSc, PhD) Experience with ML/NLP frameworks (e.g., PyTorch, TensorFlow, HuggingFace, Scikit-learn) Strong Python skills and familiarity with additional languages (e.g., Java, C++) Understanding of biomedical ontologies, knowledge graphs, or causal inference is a plus Familiarity with cloud More ❯

Platform (GCP) services, such as Vertex AI, BigQuery, Cloud Functions, and Cloud Storage. Strong programming skills in Python (preferred), Java, or similar languages, including experience with ML frameworks (TensorFlow, PyTorch, etc.). Familiarity with regulatory environments in banking/finance and understanding of regulatory change management processes. Knowledge of automation, workflow orchestration, and version control tools (e.g., Airflow, Git). More ❯

including their AI/ML services. Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging More ❯

of models. Pilot emerging technologies. Experience & Skills 2–4 years in production-level AI/ML delivery. Legal/professional services experience is a plus. AI/ML frameworks: PyTorch, TensorFlow, LangChain. Cloud: Azure (preferred), AWS, GCP. MLOps: CI/CD, model lifecycle, monitoring. Generative AI: LLMs, RAG, chat agents. Data engineering alignment: ETL, governance. Strong coding, communication, and collaboration More ❯

and data-driven tools. Drive best practices in data governance and scalable software design. Required Skills & Experience: Strong programming expertise in Python ideally - NumPy, Pandas, Scikit-learn, TensorFlow, or PyTorch Experience developing ETL/ELT pipelines and working with structured/unstructured data . Solid understanding of data structures, algorithms , and modern software engineering. Excellent communication and collaboration skills. Desirable More ❯

of models. * Pilot emerging technologies. Experience & Skills * 2-4 years in production-level AI/ML delivery. * Legal/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. * Strong coding, communication, and collaboration More ❯

engineering skills, especially in Python. Experience building robust systems for ML applications Proven track record deploying machine learning models in production (using frameworks such as Scikit-learn, TensorFlow, or PyTorch) Practical experience working with cloud infrastructure (e.g., AWS, Azure, GCP) and a good understanding of architecture, security, and scaling Hands-on experience with Docker and Kubernetes in real-world engineering More ❯

a unified ecosystem. You will promote best practices in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations More ❯

a unified ecosystem. You will promote best practices in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations More ❯

in production, building datasets, selecting and engineering features, building and optimizing algorithms You have expertise with Python and related machine learning tools, deep learning frameworks such as TensorFlow or PyTorch, and SQL-like query languages for data extraction, transformation, and loading A strong foundation in Machine Learning fundamentals, statistics, and experimentation. You have real-world experience working with large data More ❯

a unified ecosystem. You will promote best practices in AI development, governance, ethics, and integration, shaping our innovation roadmap. Additionally, you will optimize algorithms with tools like Python, TensorFlow, PyTorch, scikit-learn, and cloud services (e.g., AWS SageMaker), including data analysis, model training, and validation. You will address issues in model accuracy, bias, and integration, complying with data privacy regulations More ❯

reviews, architectural decision-making, and technical debt management Ability to establish coding standards and best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with MLOps tools and monitoring model performance More ❯

MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven ability to assess or design organizational processes for data science delivery and model management. • Excellent analytical and communication skills, with the ability to synthesize technical observations into actionable More ❯