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Pytorch Huggingface Jobs in Washington, DC (NOW HIRING)

The Metamorphosis Group Inc

Data Scientist

Vienna, VA

$150K - $195K/hr

Python NLP packages such as Spacy, Gensim, or NLTK Deep learning frameworks such as PyTorch, Tensorflow, or Keras HuggingFace Transformers library and hub Creating machine learning models that ...

Bridge Core (BCore)

Data Scientist

Mclean, VA · On-site

Demonstrated professional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras * Demonstrated professional or academic experience with the HuggingFace ...

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Pytorch Huggingface information

What are the key skills and qualifications needed to thrive as a PyTorch Huggingface engineer?

To thrive as a PyTorch Hugging Face Engineer, you need a strong background in deep learning, Python programming, and experience with machine learning frameworks, supported by a relevant degree such as computer science or engineering. Familiarity with PyTorch, Hugging Face Transformers library, version control systems like Git, and often cloud platforms (e.g., AWS, GCP) is essential, with certifications in machine learning or cloud technologies being advantageous. Strong problem-solving skills, collaboration, and clear communication help you effectively design, implement, and optimize NLP models in cross-functional teams. These skills ensure you can build state-of-the-art AI solutions efficiently, troubleshoot complex challenges, and deliver impactful results in the fast-evolving field of natural language processing.

What is the difference between Pytorch Huggingface vs Machine Learning Engineer?

AspectPytorch HuggingfaceMachine Learning Engineer
CredentialsProficiency in Python, deep learning frameworks, familiarity with NLP librariesDegree in CS, data science, or related field; experience with ML models
Work EnvironmentResearch labs, AI startups, tech companies focusing on NLP and deep learningTech companies, consulting firms, R&D departments across industries
UsageDeveloping NLP models, fine-tuning transformers, deploying AI solutionsDesigning, building, and deploying ML models across various domains

While Pytorch Huggingface specializes in NLP model development using transformer architectures, Machine Learning Engineers work across diverse ML applications. Pytorch Huggingface skills are often part of a Machine Learning Engineer's toolkit, but the roles differ in scope and focus.

What is a PyTorch Huggingface engineer?

PyTorch Hugging Face developers are professionals who specialize in building and deploying machine learning and natural language processing (NLP) models using PyTorch, an open-source deep learning framework, and the Hugging Face library, which provides a wide range of pre-trained models and tools for NLP tasks. These developers create, fine-tune, and implement models for tasks like text classification, question answering, and language generation. Their expertise includes working with model architectures such as BERT, GPT, and others, as well as integrating models into applications or research projects.

How do PyTorch Huggingface engineers typically collaborate with data scientists and researchers in a project setting?

PyTorch Huggingface engineers often work closely with data scientists and researchers to implement, fine-tune, and deploy state-of-the-art machine learning models. Collaboration involves regular discussions to understand project objectives, translating research ideas into efficient code, and iterating on model performance. Engineers are responsible for optimizing model pipelines, integrating new features, and ensuring compatibility with the Huggingface ecosystem. Effective communication and teamwork are essential, as projects usually require frequent feedback loops and joint problem-solving sessions.
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Pytorch Huggingface jobs near you

Data Scientist with Security Clearance

Navon Technologies LLC

Chantilly, VA • On-site

Other

Posted 26 days ago

Job description

Clearance required: TS SCI w/Poly Required Skills: Demonstrate professional or academic experience performing NLP tasks, including selecting the best Python libraries for a given task, choosing appropriate pre-processing actions, performing analysis, and assessing model performance. Demonstrate professional or academic experience using Python NLP packages such as Spacy, Gensim, or NLTK to analyze or process collections of documents. Demonstrate professional or academic experience with deep learning frameworks such as PyTorch, Tensorflow, or Keras. Demonstrate professional or academic experience with the HuggingFace Transformers library and hub. Demonstrate experience creating machine learning models that conduct text classification and topic modeling in Python using standard machine learning (Scikit-learn) or deep learning models. Demonstrate academic or professional experience using encoder-decoder and generative language models to perform NLP tasks. Demonstrate academic or professional experience communicating methodological choices and model results. Demonstrate professional or academic experience and proficiency with SQL to include using common table expressions, set operations, aggregated functions and nested subqueries. Demonstrate professional or academic experience with version control systems such as Github and Jenkins. Demonstrate experience leveraging GPUs for accelerated computing. Develop practical approaches for measuring performance. Assist in developing types of measure, the collection of data, analyzing the data, and presenting that data to senior leadership. Conduct advanced statistical analysis on personnel, intelligence and performance metrics. Assist in selection or development of appropriate methodology to conduct research. Analyze information and provide research findings in a manner that is easily grasped by the customer and consumers. Desired Skills: Experience writing Python scripts that pull data from web-based APIs and relational databases. Experience with cloud computing development and architecture. Experience with front-end web development frameworks such as Flask. Experience developing applications for semantic search. Experience tuning LLMs on custom data sets and applying results to specific use cases. Demonstrate professional or academic experience and proficiency with Tableau to produce visualizations and dashboards.

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