relationships with cross-functional teams. Ability to clearly communicate and present to internal and external stakeholders. Nice to have, but not essential NLP/Deep learning experience (e.g. huggingface, spaCy) Deep learning framework experience (preferably PyTorch) MLOps experience (e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Cloud platform experience, especially from an ML standpoint (AWS More ❯
Machine learning experience : Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn. Natural Language Processing (NLP) : Experience with NLP techniques and tools, such as spaCy or NLTK. Distributed systems : Knowledge of distributed systems and experience with tools like Kubernetes or Docker. Cloud services : Experience with cloud platforms like AWS, GCP, or Azure. Open source contributions More ❯
such as ACL/NIPS/EMNLP/NACL. Strong programming skills - e.g. Python, C++. Experience with Deep Learning/NLP/Machine Learning frameworks such as PyTorch, Tensorflow, Spacy, CoreNLP. Tagged as: Industry , Machine Learning , Natural Language Processing , NLP , United Kingdom More ❯
system integrations across the data stack. Design and support secure, scalable systems using network protocols (TCP/IP, OSI) Enable machine learning and AI workflows through tools like Jupyter, SpaCy, Transformers, and NLTK. Implement and support BI tools (Tableau, Power BI, Kibana) to drive actionable insights from complex data sets. If you are interested in this Big Data Engineer role More ❯
system integrations across the data stack. Design and support secure, scalable systems using network protocols (TCP/IP, OSI) Enable machine learning and AI workflows through tools like Jupyter, SpaCy, Transformers, and NLTK. Implement and support BI tools (Tableau, Power BI, Kibana) to drive actionable insights from complex data sets. If you are interested in this Big Data Engineer role More ❯
