51 to 75 of 403 Permanent PyTorch Jobs

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 modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. 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 ❯

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 ❯

reliability Stay up-to-date with emerging AI trends and technologies Skills Needed 3+ years of experience in AI and machine learning development Strong knowledge of AI frameworks (TensorFlow, PyTorch, Keras) Experience with NLP and computer vision models Strong understanding of machine learning algorithms and statistical modeling Experience with cloud-based AI platforms (AWS SageMaker, Google Cloud AI Platform) Excellent 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 ❯

audiences Active Top Secret security clearance Preferred Qualifications: Master's degree in Remote Sensing, Computer Science, Geospatial Science, Data Science, or related field Experience with machine learning frameworks (TensorFlow, PyTorch) and their application to geospatial data Experience with GIS tools such as QGIS, ArcGIS, etc. Knowledge of deep learning architectures specifically designed for remote sensing data Familiarity with hyperspectral and More ❯

native tools and services, such as Infrastructure-as-Code, Azure Monitor, Advance Threat Detection, Defender etc. Integrate and deploy AI and machine learning models and frameworks, such as TensorFlow, PyTorch, MXNet, etc., using cloud-based platforms and services, such as Azure OpenAI Service, AI Search, Machine Learning Services, etc. Introduce CI/CD pipelines that provide rapid feedback and allow 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 ❯

or a related field. 5+ years' experience in data science, with proven success delivering end-to-end projects. Proficiency in Python, R, SQL, and machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). Strong skills in data storytelling, problem-solving, and working with both structured and unstructured data. Experience with APIs, cloud platforms (especially AWS), and MLOps practices is a 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 ❯

in a leadership or architecture role, ideally with some experience in leading a Centre of Excellence or a similar initiative. • Hands-on experience with Al frameworks such as TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS, Azure, or Google Cloud. • Experience in multiple industries is advantageous (e.g., healthcare, finance, retail). Skills: AI/ML Expertise: Strong understanding of 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

programming skills in languages such as Python with a strong focus on code optimisation, 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 ❯

landscape and uses cases. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and More ❯

landscape and uses cases. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and More ❯

landscape and uses cases. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and More ❯

landscape and uses cases. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and More ❯

landscape and uses cases. Build data pipelines, models, and AI applications, using cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and More ❯