51 to 75 of 321 PyTorch Jobs in London

generative AI architectures. Production Experience: Demonstrated success in shipping and maintaining ML models in production, including performance monitoring and optimization. Strong Programming: Proficiency in Python and ML libraries (e.g., PyTorch, scikit-learn, Hugging Face, etc.); familiarity with MLOps tooling and cloud environments (AWS/GCP/Azure). Analytical & Communication Skills: Ability to clearly explain complex ideas, trade-offs, and More ❯

model as a container, update an Airflow (or Azure Data Factory) job. Review: inspect dashboards, compare control vs. treatment, plan next experiment. Tech stack Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow)SQL (Redshift, Snowflake or similar)AWS SageMaker Azure ML migration, with Docker, Git, Terraform, Airflow/ADFOptional extras: Spark, Databricks, Kubernetes. What you'll bring 3-5+ More ❯

Experience in developing and implementing algorithms for LLMs and/or diffusion models inference Programming skills in Python and experience with deep learning frameworks such as Tensor Flow or PyTorch Excellent problem-solving skills, with the ability to think creatively and critically about complex problems Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams More ❯

Statistical Analysis & Data Evaluation, you're 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 ❯

in probability & inference (e.g., Bayesian updating, time-series/state-space models, extreme-value theory). Hands-on Python for numerical analysis (numpy, pandas, xarray, SciPy/PyMC/PyTorch, or similar). Experience validating models with historical data and communicating results to non-specialists. Exposure to real-time data engineering (Kafka, Airflow, dbt) Track record turning research code into More ❯

in probability & inference (e.g., Bayesian updating, time-series/state-space models, extreme-value theory). Hands-on Python for numerical analysis (numpy, pandas, xarray, SciPy/PyMC/PyTorch, or similar). Experience validating models with historical data and communicating results to non-specialists. Exposure to real-time data engineering (Kafka, Airflow, dbt) Track record turning research code into More ❯

data-driven features into production. Your Profile: Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong understanding of More ❯

data-driven features into production. Your Profile: Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong understanding of 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 ❯

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 ❯

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 ❯

production models. Present findings to stakeholders via reports and presentations. Requirements This role is for you if: Proficiency in Python for API and model development, including frameworks like Sklearn, Pytorch, and TensorFlow. Understanding of machine learning techniques. Experience with data manipulation libraries (e.g., Pandas, Spark, SQL). Experience with version control (Git). Cloud experience (Azure, GCP, AWS). Additional More ❯

tools: CI/CD, version control (git), testing frameworks, MLOps Comfortable working with Docker and containerised applications Experience with data science Python libraries such as Scikit-learn, Pandas, NumPy, Pytorch etc. Experience using AWS or similar cloud computing platform Great communicator - convey complex ideas and solutions in clear, precise and accessible ways Team player who cares about accelerating not only 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 ❯

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 ❯

Science, Machine Learning, or a related field Strong software engineering skills with a proven track record of building complex systems Expertise in Python and experience with deep learning frameworks (PyTorch preferred) Familiarity with large-scale machine learning, particularly in the context of language models Ability to balance research goals with practical engineering constraints Strong problem-solving skills and a results More ❯

Data Integration & ETL: Data Pipelining Tools: Apache NiFi, Apache Kafka, and Apache Flink. ETL Tools: AWS Glue, Azure Data Factory, Talend, and Apache Airflow. AI & Machine Learning: Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, and MXNet. AI Services: AWS SageMaker, Azure Machine Learning, Google AI Platform. DevOps & Infrastructure as Code: Containerization: Docker and Kubernetes. Infrastructure Automation: Terraform, Ansible, and AWS CloudFormation. More ❯

Analytic Science - Pre-Sales Lead Scientist Analytic Science - Pre-Sales Lead Scientist Apply locations: London, United Kingdom Time type: Full time Posted on: Posted Yesterday Job requisition id: 30622 About FICO FICO (NYSE: FICO) is a leading global analytics software More ❯

AI GPT Family of models 4o/4/3, Embeddings + Vector Search o Databases and ETL: Azure Storage Account, PostgreSQL, Cosmos o Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) o Knowledge of cloud platforms (AWS SageMaker, Google AI Platform) o Expertise in data pre-processing, feature engineering, and model evaluation o Understanding of software engineering principles (version More ❯

AI GPT Family of models 4o/4/3, Embeddings + Vector Search o Databases and ETL: Azure Storage Account, PostgreSQL, Cosmos o Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn) o Knowledge of cloud platforms (AWS SageMaker, Google AI Platform) o Expertise in data pre-processing, feature engineering, and model evaluation o Understanding of software engineering principles (version More ❯

technically strong, but solution-oriented, strategically minded, and able to communicate insights clearly to both technical and non-technical audiences. Requirements: Advance Python (Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch) & SQL skills. (Snowflake a plus) Experience with Data warehousing and database technologies Solid machine learning experience (modelling to deployment) Cloud exposure (GCP/AWS/Azure) Excellent communication & stakeholder skills More ❯

bring: Experience within a startup with a self-starter mindset. Proven track record with NLP applications and transformer-based models like BERT or GPT. Advanced proficiency in Python, including PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. 📩 Interested? Apply below or email More ❯