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Hugging Face Jobs in Washington (NOW HIRING)

For language-based AI, expertise in NLP techniques and libraries such as NLTK, spaCy, and Hugging Face Transformers is key. * Cloud Computing and MLOps: Knowledge of cloud platforms (AWS, GCP, Azure ...

AI/ML Engineer

Washington, DC ยท Remote

$190K - $220K/yr

Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain. * Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or ...

AI/ML Engineer

Washington, DC ยท Remote

$190K - $220K/yr

Build, train, and fine-tune models using frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, and LangChain. * Develop and operationalize MLOps pipelines (MLflow, Kubeflow, DVC, or ...

Software Engineer II

Herndon, VA ยท On-site

$100K - $137K/yr

Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate. * Experience with distributed training frameworks such as DeepSpeed, FSDP, or ...

Software Engineer II

Herndon, VA ยท On-site

$100K - $137K/yr

Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate. * Experience with distributed training frameworks such as DeepSpeed, FSDP, or ...

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Hugging Face information

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How much do hugging face jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for hugging face in Washington is $17.51, according to ZipRecruiter salary data. Most workers in this role earn between $14.71 and $20.67 per hour, depending on experience, location, and employer.

What is the difference between Hugging Face vs Machine Learning Engineer?

AspectHugging FaceMachine Learning Engineer
Required CredentialsTypically requires knowledge of NLP, deep learning, and Python; certifications are optionalRequires degrees in CS or related fields; experience with ML frameworks; certifications beneficial
Work EnvironmentCollaborative, research-focused, often in tech companies or startupsDevelopment, deployment, and optimization of ML models in various industries
Employer & Industry UsageUsed by AI/ML companies, research labs, and open-source communitiesEmployed across tech, finance, healthcare, and other sectors implementing ML solutions

Hugging Face primarily focuses on NLP tools, libraries, and open-source models, serving as a platform for AI research and development. Machine Learning Engineers develop, implement, and optimize ML models across various domains. While Hugging Face offers resources and tools that ML Engineers use, the roles differ: Hugging Face is a platform, whereas Machine Learning Engineer is a job role involving hands-on model development and deployment.

What are popular job titles related to Hugging Face jobs in Washington? For Hugging Face jobs in Washington, the most frequently searched job titles are:
What cities in Washington are hiring for Hugging Face jobs? Cities in Washington with the most Hugging Face job openings:
Infographic showing various Hugging Face job openings in Washington as of July 2026, with employment types broken down into 1% As Needed, 77% Full Time, 20% Part Time, and 2% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $36,414 per year, or $17.5 per hour.

Gen AI Developer with Python for Reston VA

Hexaware Technologies, Inc

Reston, VA โ€ข On-site

$52.25 - $72/hr

Other

Re-posted 8 days ago


Job description

ROLE: AWS Python + Gen AI Developer
Location- Reston, VA - Day one onsite
Technical Skills:
  • 10+ years of experience in software development, with 5+ years in AWS and AI/ML technologies.
  • Strong proficiency in Python programming and frameworks (e.g., Flask, FastAPI, Django).
  • Hands-on experience with AWS services (e.g., Lambda, S3, EC2, IAM, CloudFormation).
  • Experience with GenAI tools (e.g., OpenAI, Hugging Face, or custom LLMs).
  • Knowledge of DevOps tools like Docker, Kubernetes, and CI/CD pipelines.
  • Familiarity with RESTful APIs and integration of AI/ML solutions.
  • Strong debugging and problem-solving skills in production environments.
  • Soft Skills:
  • Excellent communication and collaboration skills.
  • Ability to work in a fast-paced environment and manage competing priorities.