251 to 275 of 2,013 PyTorch Jobs

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 More ❯

degree in a quantitative field (e.g., Computer Science, Machine Learning, Statistics, Physics, Mathematics) or equivalent industry experience. Strong background in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX). Experience with distributed computing platforms (AWS, GCP, Azure, or on-prem clusters). Ideal: Kubernetes and Docker experience for scalable, reproducible workflows. More ❯

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 More ❯

You Have Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Technical Product Manager. Familiarity with AI frameworks such as PyTorch or TensorFlow. Contributions to open-source projects, particularly in the space of DevOps or AI. Benefits France Competitive cash salary and equity. Food: Daily lunch More ❯

you have: • Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Technical Product Manager • Familiarity with AI frameworks such as PyTorch or TensorFlow • Contributions to open-source projects, particularly in the space of DevOps or AI Benefits We have local offices in Paris, London, Marseille and More ❯

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 More ❯

Vision Solid understanding of fundamentals of statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, autoencoders etc.) Extensive experience with pytorch, numpy, pandas Knowledge of open source datasets and benchmarks in NLP/Computer Vision Hands-on experience in implementing distributed/multi-threaded/scalable More ❯

Docker. Plus, a strong opinion on your IDE/editor of choice is welcome ;) Familiarity with modern machine learning tools, for instance TensorFlow, Keras, PyTorch or SKLearn. Commercial experience with these is not essential. Excellent communication skills; both in customer-facing and internal team communication. Knowledge of MLOps is not More ❯

problem-solving skills is considered as a plus. Technical Skills: Strong programming skills in Python and SQL . Expertise in machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn). Hands-on experience with big data technologies and cloud platforms (AWS preferred; Azure is a plus). Experience deploying ML models More ❯

AI, CVAT, Prodigy, Postgres, etc.). Experience designing, maintaining, and scaling ML pipelines and tooling. Proficiency in Python and experience with ML libraries (e.g., PyTorch, TensorFlow, scikit-learn). Experience with the Go programming language Experience with experiment tracking tools (e.g., MLflow, Weights & Biases). Strong knowledge of DevOps, CI More ❯

required: Programming: Proficiency in Python (preferred) and/or other relevant languages (e.g., R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with More ❯

Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated ability to More ❯

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 More ❯

required: Programming: Proficiency in Python (preferred) and/or other relevant languages (e.g., R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with More ❯

Python, R, and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated ability to More ❯

You must be able to hold or gain a UK government security clearance. Preferred Technical And Professional Experience Experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn). Familiarity with big data technologies (Hadoop, Spark). Background in data science, IT consulting, or a related field. AWS Certified Big Data 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 ❯

and practical application. Strong understanding of statistical concepts (e.g., hypothesis testing, sampling, probability distributions). Experience with ML libraries (e.g., scikit-learn; TensorFlow or PyTorch a plus). Proven track record of deploying ML models to production environments. Experience with software development concepts including Version Control (e.g., Git), CI/ More ❯

e.g., librosa, torch audio, or similar). Must understand machine learning techniques, including deep learning architectures (e.g., CNNs, RNNs, GANs) and relevant frameworks (e.g., PyTorch). Must be proficiency in data preprocessing, feature extraction, and data augmentation techniques for audio. Expected to have strong problem-solving skills and ability to More ❯

experience, or an MS with significant industry or research experience in the field Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas) Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with More ❯

match you must have: 1+ years in a Machine Learning or ML Engineering role, with hands-on experience in deep learning frameworks (e.g., TensorFlow, PyTorch). Motivated recent graduates are encouraged to apply! A degree in Mathematics, Engineering, Statistics, Computer Science, Physics, or a related field. An advanced degree is More ❯

and practical application. Strong understanding of statistical concepts (e.g., hypothesis testing, sampling, probability distributions). Experience with ML libraries (e.g., scikit-learn; TensorFlow or PyTorch a plus). Proven track record of deploying ML models to production environments. Experience with software development concepts including Version Control (e.g., Git), CI/ More ❯

and delivering complex software projects. Strong programming skills in languages such as Python, Java, or C++. Applying machine learning frameworks and tools (e.g., TensorFlow, PyTorch). Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Ability to work in a fast-paced, dynamic environment. Hands-on involvement with More ❯

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. More ❯

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. More ❯