26 to 50 of 765 Remote PyTorch Jobs

related discipline. 3+ years of professional experience developing and deploying machine learning and AI models. Proficiency in Python and AI frameworks such as TensorFlow, PyTorch, or Scikit-learn. Working knowledge of AI development, monitoring, and agentic frameworks (e.g., LangChain, LangSmith, LangGraph, or similar). Expert knowledge of test-driven development More ❯

related discipline. 3+ years of professional experience developing and deploying machine learning and AI models. Proficiency in Python and AI frameworks such as TensorFlow, PyTorch, or Scikit-learn. Working knowledge of AI development, monitoring, and agentic frameworks (e.g., LangChain, LangSmith, LangGraph, or similar). Expert knowledge of test-driven development More ❯

related discipline. 3+ years of professional experience developing and deploying machine learning and AI models. Proficiency in Python and AI frameworks such as TensorFlow, PyTorch, or Scikit-learn. Working knowledge of AI development, monitoring, and agentic frameworks (e.g., LangChain, LangSmith, LangGraph, or similar). Expert knowledge of test-driven development 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 ❯

related fields (MSc/PhD preferred). Experience with computational linguistics is highly desirable. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, Scikit-learn. Experience deploying AI solutions using AWS, GCP, or Azure. Hands-on experience with MLOps, CI/CD, and model performance monitoring. Background More ❯

to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for More ❯

to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for More ❯

RFI/RFP processes, preparing bids, and delivering presentations. Familiarity with data science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., Keras, TensorFlow, PyTorch, scikit-learn). Experience deploying solutions on Cloud platforms (AWS, Azure, Google Cloud) using provisioning tools like Terraform. Proven ability to deploy technologies such as More ❯

Exposure to computational linguistics is highly desirable, from both academic research and commercial perspectives. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS, GCP, or Azure. Hands-on experience with MLOps, CI/CD for More ❯

documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). More ❯

documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). More ❯

platforms like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Technical Skills – Good to have: • Expertise in any one framework (TensorFlow, Pytorch, Keras) • Experience in a statistical programming language (e.g. R or Python) and applied machine learning and AI techniques (i.e computer vision, deep learning, conversational AI 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 ❯

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 with SQL and familiarity with database management practices to handle and query large datasets efficiently. Knowledge of machine learning frameworks like TensorFlow or PyTorch and libraries such as scikit-learn. Proficiency in using data analysis and visualization tools to interpret data and present insights effectively. Proven experience as a 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 ❯

to-end ML pipelines Hand-on experience in Python data science ecosystem (e.g. numpy, scipy, pandas, scikit-learn, matplotlib etc.) Deep Learning frameworks (TensorFlow, PyTorch, MLX) Popular classification and regression techniques Unsupervised learning & matrix factorisation algorithms Natural Language Processing (NLP) and document processing Generative AI (open and closed source) - LLM More ❯

applications and LLM-based chatbots and agents Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries ( Pytorch, Tensorflow) from early conceptualisation to full deployment in scalable production environments Ability to design, deploy and maintain ML solutions on modern frameworks to meet functional More ❯

Experience working with Azure AI services (or AWS, GCP) and AI model deployment. Understanding of generative AI, LLMs, NLP, and machine learning frameworks (TensorFlow, PyTorch, Hugging Face, etc.). Understanding of the AI application lifecycle, including application prototyping, productionising and monitoring. Python/Go/R. Problem solving skills. Strong More ❯