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Pytorch Huggingface Jobs in Washington (NOW HIRING)

Accenture LLP

Senior AI/ML & Data Engineer

Chantilly, VA · On-site

$108K - $147K/yr

Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Scikit-learn). * Hands on experience with LLM development (OpenAI, Anthropic, Bedrock, Azure OpenAI, HuggingFace Transformers)

Bespoke Technologies, Inc

Data Scientist

Mclean, VA · On-site

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

The Metamorphosis Group Inc

Data Scientist

Vienna, VA · On-site

$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

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

Compunnel

AI QE Architect

Bethesda, MD · On-site

... LangChain, HuggingFace, Transformers, and OpenAI/Ollama APIs. • Experience with agentic AI ... PyTorch, TensorFlow, Scikit-Learn, and NLP/CV libraries such as NLTK, BART, or OpenCV. • ...

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Pytorch Huggingface information

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.

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.

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.

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 are popular job titles related to Pytorch Huggingface jobs in Washington?

For Pytorch Huggingface jobs in Washington, the most frequently searched job titles are:

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The top searched job categories for Pytorch Huggingface jobs in Washington are:

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Cities in Washington with the most Pytorch Huggingface job openings:

Pytorch Huggingface jobs near you

Machine Learning Engineer - Computer Vision

CaseGuard

Arlington, VA • On-site

Full-time

Re-posted 10 days ago

Job description

Job Summary:
CaseGuard is a software company that helps various agencies manage their media redaction needs. They are seeking a highly skilled Machine Learning Engineer specializing in Computer Vision to design, implement, and optimize vision-based AI solutions focused on image and video processing.
Responsibilities:
• Design, develop, and deploy computer vision models for tasks such as object detection, object tracking, video segmentation, and facial recognition.
• Optimize and fine-tune deep learning algorithms for real-time performance.
• Work closely with the software engineers and product teams to identify opportunities for leveraging data.
• Collect, clean, and preprocess large datasets to prepare for model training and evaluation.
• Evaluate and optimize machine learning models for accuracy, performance, and scalability.
• Deploy models into production environments and monitor their performance to ensure reliability.
• Stay up-to-date with the latest advancements in computer vision and artificial intelligence.
• Collaborate with cross-functional teams to integrate machine learning solutions into business processes.
• Document processes, models, and implementations to ensure reproducibility and scalability.
Qualifications:
Required:
• Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Experience in deep learning models, their training, and hyperparameter tuning using libraries such as TensorFlow, PyTorch, and Transformers or other Huggingface tools.
• Experience with data manipulation tools such as Pandas, NumPy, and SQL.
• Strong programming skills in Python and C++.
• Experience in MLOps principles and model deployment and instrumentation on cloud platforms such as AWS, Azure, or Google Cloud for model deployment and knowledge with efficient serving tools such as ONNX, triton, and vllm.
• Proficiency in working with image and video data, including preprocessing and augmentation techniques.
• Strong understanding of machine learning algorithms, including supervised and unsupervised learning and deep learning.
• Strong communication skills and the ability to work collaboratively in a team environment.
Preferred:
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• Experience with version control systems such as Git.
• Understanding software engineering best practices, including code review, testing, and documentation.
• Experience with Large Language Models (LLMs) is a great plus.
• Experience with data annotation tools and processes.
Company:
CaseGuard is a management solutions company. Founded in , the company is headquartered in Sterling, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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