1 to 25 of 1,245 Remote Permanent PyTorch Jobs

/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs). Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn. Hands-on experience deploying ML models in production environments using MLOps principles. Expertise in relevant AI/ML applications, such as predictive More ❯

a wide range of ML algorithms. Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g. TensorFlow, PyTorch). Proficiency in programming languages such as Python, SQL and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Experience with LLM More ❯

a wide range of ML algorithms Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g. TensorFlow, PyTorch) Proficiency in programming languages such as Python, SQL and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Strong software engineering skills More ❯

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 More ❯

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 More ❯

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 More ❯

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 More ❯

monitoring and maintenance +2 years of experience with Amazon Web Services (AWS) and/or Google Cloud Platform (GCP) Experience with at least 80%: PyTorch, Tensorflow, LangChain, scikit-learn, Redis, Elasticsearch, Amazon SageMaker, Google Vertex AI, Weights & Biases, FastAPI, Prometheus, Grafana, Apache Kafka, Apache Airflow, MLflow, KubeFlow Ability to break More ❯

like GANs, VAEs, transformers, or diffusion models Proficiency in programming languages such as Python, R, or Julia, and experience with ML frameworks like TensorFlow, PyTorch, or Keras Strong understanding of deep learning architectures, neural networks, and statistical modeling techniques Experience with cloud platforms (AWS, Azure, Google Cloud) and MLops tools More ❯

hyper-scaler platforms, e.g., AWS, Azure, GCP); building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; C/C++ for computer vision, geometry processing, or scientific computing; software engineering concepts and best practices (e.g., versioning More ❯

Proven experience building and deploying RAG systems and/or LLM-powered applications in production environments. Proficiency in Python and ML libraries such as PyTorch, Hugging Face Transformers, or TensorFlow. Experience with vector search tools (e.g., FAISS, Pinecone, Weaviate) and retrieval frameworks (e.g., LangChain, LlamaIndex). Hands-on experience with More ❯

Stay informed on advancements in AI and integrate the latest technologies into solutions. Key Skills & Experience: Strong programming skills in Python (experience with TensorFlow, PyTorch, or similar frameworks). Expertise in deploying models on cloud platforms (Azure) or containerised environments (Docker, Kubernetes). Proficiency in machine learning algorithms and advanced More ❯

Science, Artificial Intelligence, Data Science, or a related field (or equivalent experience). Technical Skills: Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong understanding of NLP techniques, including tokenization, embeddings, transformers, and attention mechanisms. Experience in retraining and fine-tuning LLMs using large-scale datasets. Familiarity More ❯

like GANs, VAEs, transformers, or diffusion models. Proficiency in programming languages such as Python, R, or Julia, and experience with ML frameworks like TensorFlow, PyTorch, or Keras. Strong understanding of deep learning architectures, neural networks, and statistical modeling techniques. Experience with cloud platforms (AWS, Azure, Google Cloud) and MLops tools More ❯

science. • Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. • Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). • Proficiency in Python, R, or other relevant programming languages. • Proficiency in working with large datasets More ❯

experience in AI development. Strong understanding of machine learning, deep learning, and other AI techniques. Proficiency in Python and relevant AI libraries (e.g., TensorFlow, PyTorch). Experience with data processing and analysis tools (e.g., Pandas, SQL). Experience with cloud platforms such as Amazon Web Services, Azure, or Google Cloud More ❯

Scikit-learn etc.). • Solid understanding of core machine learning concepts • Hands-on experience with at least one major deep learning framework (e.g., TensorFlow, PyTorch, Keras). • Proven understanding of software engineering principles, data structures, algorithms, and design patterns. • Experience with version control systems (e.g., Git). • Excellent problem-solving More ❯

particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for More ❯

At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure (ideally AWS), Kubernetes, and More ❯

experience in AI development. Strong understanding of machine learning, deep learning, and other AI techniques. Proficiency in Python and relevant AI libraries (e.g., TensorFlow, PyTorch). Experience with data processing and analysis tools (e.g., Pandas, SQL). Experienced with cloud platforms such as Amazon Web Services, Azure, or Google Cloud More ❯

experience in AI development. Strong understanding of machine learning, deep learning, and other AI techniques. Proficiency in Python and relevant AI libraries (e.g., TensorFlow, PyTorch). Experience with data processing and analysis tools (e.g., Pandas, SQL). Experienced with cloud platforms such as Amazon Web Services, Azure, or Google Cloud More ❯

organization is considered an asset. Other skills we are searching for are: Programming Skills: Proficiency in Python, data analytics, deep learning (Scikit-learn, Pandas, PyTorch, Jupyter, pipelines), and practical knowledge of data tools like Databricks, Ray, Vector Databases, Kubernetes, and workflow scheduling tools such as Apache Airflow, Dagster, and Astronomer. More ❯

ML algorithms-ranging from classical techniques to deep learning methods. Proven experience in training, fine-tuning, and deploying neural network models using frameworks like PyTorch, with a focus on optimizing performance and scalability for realworld applications. Demonstrated ability to mentor and lead technical teams, influence technical strategy, and drive innovation More ❯

AI practices, including fairness, interpretability, and compliance. What We're Looking For Proven experience in machine learning model development and deployment (e.g., Python, TensorFlow, PyTorch, Scikit-learn). Solid grasp of ML concepts: supervised/unsupervised learning, NLP, computer vision, time-series, etc. Experience working with cloud platforms (AWS, Azure More ❯

AI practices, including fairness, interpretability, and compliance. What We're Looking For Proven experience in machine learning model development and deployment (e.g., Python, TensorFlow, PyTorch, Scikit-learn). Solid grasp of ML concepts: supervised/unsupervised learning, NLP, computer vision, time-series, etc. Experience working with cloud platforms (AWS, Azure More ❯