1

Hugging Face Jobs in Washington, DC (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 ...

Data Scientist

Mclean, VA · On-site

$177 - $217/hr

Leverage the Hugging Face Transformers library and model hub to develop state-of-the‑art language models and text analytics solutions. * Create and maintain machine learning models that perform ...

Leverage the Hugging Face Transformers library and model hub to develop state-of-the-art language models and text analytics solutions. * Create and maintain machine learning models that perform text ...

Data Scientist

Mclean, VA · On-site

$177K - $216K/yr

Leverage the Hugging Face Transformers library and model hub to develop state-of-the-art language models and text analytics solutions. * Create and maintain machine learning models that perform text ...

next page

Showing results 1-20

Hugging Face information

See Washington, DC salary details

$10

$17

$23

How much do hugging face jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for hugging face in Washington, DC is $17.50, 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, DC?

For Hugging Face jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Hugging Face jobs in Washington, DC look for?

The top searched job categories for Hugging Face jobs in Washington, DC are:

Infographic showing various Hugging Face job openings in Washington, DC as of August 2026, with employment types broken down into 72% Full Time, 26% Part Time, and 2% Contract. Highlights an 84% Physical, 1% Hybrid, and 15% Remote job distribution, with an average salary of $36,277 per year, or $17.4 per hour.

Gen AI Developer with Python for Reston VA

Hexaware Technologies, Inc

Reston, VA • On-site

$52.25 - $72/hr

Other

This job post has expired today. Applications are no longer accepted.


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.