351 to 375 of 421 PyTorch Jobs

Information Systems team, youll collaborate with software engineers, data scientists, and business stakeholders to implement real-world intelligent systems using technologies such as Python, PyTorch, and Azure. Do you have? Experience developing AI/ML solutions using Python? Knowledge of frameworks such as PyTorch (preferred), TensorFlow, or Scikit-learn? Experience ...

Responsibilities: Build and deploy production-grade machine learning models (classification, regression, NLP, etc.) Develop reusable ML pipelines using Python and libraries like scikit-learn, PyTorch or TensorFlow Collaborate with cross-functional teams to integrate ML solutions into real applications Conduct model performance analysis and iterate based on business feedback Automate … experience deploying ML models into production environments Strong understanding of algorithms, data structures, and model evaluation metrics Experience with frameworks such as scikit-learn, PyTorch, TensorFlow Comfortable working in cloud environments ( AWS, GCP or Azure ) Familiarity with containerization ( Docker ) and CI/CD for ML workflows Exposure to time series ...

cross-functional teams and mentor junior engineers Tools: Microsoft Fabric, Azure ML, MLflow, Spark, Python, SQL, CI/CD, and ML frameworks like TensorFlow, PyTorch, Scikit-learn Scope: End-to-end AI/ML solutions for multi-product, multi-region strategy Report To: Head of Data Science/CTO Location … pipelines and architectures Hands-on with Microsoft Fabric, Azure ML, MLflow, Spark, Python & SQL Proven ability to develop, deploy and manage ML models (TensorFlow, PyTorch, Scikit-learn) Solid grounding in data modelling, governance, security and responsible AI Excellent problem-solving and communication skills in fast-paced environments Nice to have ...

cross-functional teams and mentor junior engineers Tools: Microsoft Fabric, Azure ML, MLflow, Spark, Python, SQL, CI/CD, and ML frameworks like TensorFlow, PyTorch, Scikit-learn Scope: End-to-end AI/ML solutions for multi-product, multi-region strategy Report To: Head of Data Science/CTO Location … pipelines and architectures Hands-on with Microsoft Fabric, Azure ML, MLflow, Spark, Python & SQL Proven ability to develop, deploy and manage ML models (TensorFlow, PyTorch, Scikit-learn) Solid grounding in data modelling, governance, security and responsible AI Excellent problem-solving and communication skills in fast-paced environments Nice to have ...

What We’re Looking For 0–3 years of experience in Machine Learning, Data Science, or NLP/LLM. Strong Python skills; exposure to PyTorch/TensorFlow/Hugging Face. (Bonus) understand fundamentals of deep learning, LLMs, and MLOps, vector databases, embeddings, or retrieval-augmented generation (RAG). Loves solving … needle. Machine Learning Engineer, LLM Engineer, AI Engineer, Artificial Intelligence, Deep Learning, NLP, Natural Language Processing, Large Language Models, Generative AI, GenAI, Neural Networks, PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, RAG, Retrieval-Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing ...

Machine Learning Engineer Machine Learning Engineer - AI for Advanced Materials - Oxford (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join … client is seeking Machine Learning Engineers with experience in some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile ...

An ML Research Engineer is sought to join an innovative AI research team in Surrey, contributing to the development of advanced AI models and algorithms for speech, audio, and generative AI applications. This is a ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

Internal tooling, benchmarks, and evaluation frameworks Ideal background: Strong C++ with CUDA and solid Python Deep ML systems optimisation experience (not just model training) PyTorch, large-scale inference, multi-GPU/multi-node systems HPC, embedded, or performance-critical environments ML/Machine Learning/C++/Python/CUDA ...

interfaces that present real-time data to end users. • Implement data acquisition and visualisation pipelines for hardware-connected systems. • Integrate machine learning models using PyTorch and support inference testing. • Deploy software on Linux-based platforms and maintain robust build and release workflows. • Collaborate closely with embedded, electronics and mechanical engineers ...

solution architecture (3+ years in AI/ML) Strong AWS experience (SageMaker, Bedrock, Lambda, EKS, Glue) Expertise in Python/Java and ML frameworks (PyTorch, TensorFlow, HuggingFace) Hands-on with Generative AI, LLMs, RAG architectures, vector databases (Pinecone, Cosmos, OpenSearch) Experience designing ML pipelines end-to-end Experience with … CloudWatch, Prometheus, Grafana, Open Telemetry Top Skills: AI Architect, AWS SageMaker, AWS Bedrock, Machine Learning Pipelines, Generative AI, LLM, RAG, RLHF, Fine-Tuning, TensorFlow, PyTorch, Hugging Face, Vector Databases, Pinecone, EKS, Kubernetes, Cloud-Native Architecture, ML Ops, CI/CD, AWS Lambda, AI Workloads, Prometheus, Grafana, Open Telemetry. ...

Data Lake architectures. Develop and deploy ML models (supervised, unsupervised, tree-based, ensemble methods). Implement deep learning architectures (CNN, RNN, LSTM) using TensorFlow, PyTorch, and Keras. Work on NLP and Generative AI tasks including embeddings, transformers (BERT, GPT), and OpenAI APIs. Integrate agentic AI frameworks (LangChain, LangGraph, MCP, Bedrock … Trees, Random Forest, Gradient Boosted Trees, XGBoost Unsupervised Models: K-Means, DBSCAN, Hierarchical Clustering Deep Learning ANN, CNN, RNN, LSTM Frameworks: TensorFlow (Gradient Tape), PyTorch NN, Keras Sequential NLP & Generative AINLTK, CBoW, n-grams, Word2Vec, TF-IDF, Word Embeddings Transformers: BERT, ELMo OpenAI Models: GPT-3.5 Turbo ...