1 to 25 of 76 PyTorch Jobs with Hybrid or WFH Options

data. Proficiency in Python, NLP libraries (NLTK, SpaCy), Hugging Face Transformers, data science libraries (Numpy, Pandas) and deep learning frameworks (TensorFlow and/or PyTorch). Experience crawling, cleaning, processing unstructured data. Understanding of CI/CD & Devops principles. Familiarity with software development best practices, version control systems (e.g., Git more »

architectures. Proficiency in Python, NLP libraries (NLTK, SpaCy), Hugging Face Transformers, data science libraries (Numpy, Pandas) and deep learning frameworks (TensorFlow and/or PyTorch). Experience crawling, cleaning, processing unstructured data. Strong background in machine learning algorithms and deep learning architectures relevant to NLP tasks. Experience with model optimisation more »

data science, e.g., SQL, R and Python alongside the ability to use tools and packages such as Alteryx, Jupyter notebook, R Markdown, TensorFlow, Keras, Pytorch, Apache Spark etc. oPractical proficiency in producing reproducible code and pipelines including documentation, governance and assurance frameworks, automation and code review using tools such as more »

data science, e.g., SQL, R and Python alongside the ability to use tools and packages such as Alteryx, Jupyter notebook, R Markdown, TensorFlow, Keras, Pytorch, Apache Spark etc. oPractical proficiency in producing reproducible code and pipelines including documentation, governance and assurance frameworks, automation and code review using tools such as more »

data science, e.g., SQL, R and Python alongside the ability to use tools and packages such as Alteryx, Jupyter notebook, R Markdown, TensorFlow, Keras, Pytorch, Apache Spark etc. oPractical proficiency in producing reproducible code and pipelines including documentation, governance and assurance frameworks, automation and code review using tools such as more »

a related field. Strong background in deep learning, with hands-on experience in developing and implementing deep neural networks using frameworks such as TensorFlow, PyTorch, or Keras. Proficiency in programming languages such as Python and experience with data manipulation and analysis libraries (e.g., pandas, NumPy, scikit-learn). Solid understanding more »

environment is highly preferred. Technical Expertise: Expert knowledge of Python and its popular libraries like NumPy, Pandas, SciPy, Scikit-learn, Statsmodels ,QuantLib ,Matplotlib, TensorFlow ,PyTorch ,Zipline and Dash. Proficient in systems that handle high-throughput, low-latency data. Experience with big data technologies, SQL, and NoSQL databases. Familiarity with cloud more »

Azure OpenAI, Azure Synapse Analytics, etc., is a plus. Knowledge Graphs and LLM’s is advantageous. Machine learning algorithms and frameworks (e.g., TensorFlow, Keras, PyTorch). Version control systems (e.g., Git) and CI/CD practices. Ability to lead and mentor data science teams, driving collaboration and knowledge sharing. If more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

language Demonstrated knowledge of RAG (Retrieval Augmented Generation), Zeroshot (ZSL), Multishot prompting and other prompt engineering approaches, common AI/ML frameworks (e.g., TensorFlow, PyTorch), GIT, Docker and vector databases Demonstrated knowledge of cloud AI services (AWS SageMaker, GCP AI Platform, Azure ML) and APIs Proficiency in using and configuring more »

Engineer or similar role, with a strong emphasis on engineering Proficiency in Python programming and experience with relevant libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, etc. Extensive experience of deploying a cloud platform (GCP, AWS or Azure) Strong proficiency in NumPy for numerical computing and data manipulation tasks. more »

Learning Engineer or similar role, with a strong emphasis on engineeringProficiency in Python programming and experience with relevant libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, etc.Extensive experience of deploying a cloud platform (GCP, AWS or Azure)Strong proficiency in NumPy for numerical computing and data manipulation tasks.Proven knowledge more »

NLTK, scikit, SpaCy > Machine Learning NLP, ML algos, Hugging Face, large language models (LLMs), Retrieval Augmented Generation), learning algorithms, neural networks, ML frameworks (TensorFlow, PyTorch) > Strong IaC experience, using Terraform or CloudFormation > Data Wrangling > Systems: Linux, APIs, infrastructure design load balancing, VMs, PostgreSQL, vector dbs. Senior ML Learning Engineer desirable more »

NLTK, scikit, SpaCy Machine Learning NLP, ML algos, Hugging Face, large language models (LLMs), Retrieval Augmented Generation), learning algorithms, neural networks, ML frameworks (TensorFlow, PyTorch) Data Wrangling Systems: Linux, APIs, infrastructure design load balancing, VMs, PostgreSQL, vector dbs. ML Learning Engineer desirable skills: Version control (Git), computer vision libraries, Big more »

in programme) Python Programming Language – 4-5+ years of experience in a Data Science setting Experience in AI/Machine Learning + either PyTorch/TensorFlow Experience with NumPy/Pandas Matplotlib SQL/MySQL/database experience Please note, this is a hybrid position. Candidates who cannot travel more »

learning with one or more of the following: generative models, segmentation, object detection, classification, model optimisations Experience working with machine learning frameworks such as PyTorch, TensorFlow or Keras Experienced with foundational Large Language Models (LLMs) APIs Experience in LLMs quantitative analysis Exposure to MLOps practices Familiarity with version control systems more »

in a tech team using a diverse tech stack including: Backend: Python, FastAPI, PostgreSQL, Vespa, SQLAlchemy, Flask. Frontend: React, Next.js. Data Science: Python, Jupyter, PyTorch, Pandas, Spacy, Huggingface, Numpy, Streamlit, Weights and biases. Infra: Pulumi, Docker, AWS AppRunner, Step Functions, Grafana cloud monitoring, Prefect. Who you are Must haves: Experience more »

be working in a tech team using a diverse tech stack including:Backend: Python, FastAPI, PostgreSQL, Vespa, SQLAlchemy, Flask.Frontend: React, Next.js.Data Science: Python, Jupyter, PyTorch, Pandas, Spacy, Huggingface, Numpy, Streamlit, Weights and biases.Infra: Pulumi, Docker, AWS AppRunner, Step Functions, Grafana cloud monitoring, Prefect.Who you areMust haves:Experience using Python with more »