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Hugging Face Jobs in Washington, DC (NOW HIRING)

Theory AI

Machine Learning Engineer

Washington, DC · Remote

$3.0K/wk

PyTorch or JAX, Hugging Face, experiment tracking * Practical experience evaluating LLMs beyond a single aggregate score * Able to explain a technical trade-off to a client who is not an engineer ...

JCD Staffing

Full Stack Developer

Washington, DC · On-site

Experience with Azure OpenAI, OpenAI API, Hugging Face, or other AI/LLM APIs * Experience with GitHub Actions, Azure DevOps, or similar CI/CD tools * Familiarity with TypeScript, Azure cloud services ...

Theory AI

Machine Learning Engineer

Washington, DC · Remote

$3.0K/wk

PyTorch or JAX, Hugging Face, experiment tracking * Practical experience evaluating LLMs beyond a single aggregate score * Able to explain a technical trade-off to a client who is not an engineer ...

Theory AI

Machine Learning Engineer

Washington, DC · Remote

$3.0K/wk

PyTorch or JAX, Hugging Face, experiment tracking * Practical experience evaluating LLMs beyond a single aggregate score * Able to explain a technical trade-off to a client who is not an engineer ...

Info Way Solutions

Data scientist With AI

Reston, VA · On-site

... or Hugging Face. • Strong knowledge of statistical analysis, machine learning, and deep learning techniques. • Experience with cloud platforms (AWS, Azure, or GCP) and tools like Docker ...

Cynnovative

ML Engineer (LLM Systems)

Arlington, VA · On-site

$120 - $150/hr

Proficiency in Python and ML frameworks (e.g., PyTorch, Hugging Face) * Experience with distributed systems or large-scale compute environments * Experience with containerization and cloud platforms ...

AKIVA AI, LLC

AI Research Scientist

Leesburg, VA · On-site

$150 - $200/hr

Experience with reinforcement learning, mechanistic interpretability, or alignment research Tools, Technologies & Frameworks PyTorch, JAX, Hugging Face Transformers, Weights & Biases, Ray, custom ...

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How much do hugging face jobs pay per hour?

As of Sep 4, 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?

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Hugging Face jobs near you
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.

AI/ML Engineer

Accenture Federal Services

Washington, DC • On-site

Full-time

Re-posted 10 hours ago

Accenture Federal Services rating

8.7

Company rating: 8.7 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

51st of 500 rated business services

Job description

The work: 

  • Develop MLOps frameworks and workflows for a variety of domains and applications 
  • Build, train, deploy, and maintain machine learning models in production environments.
  • Design, develop, and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, validation, deployment, and monitoring.
  • Implement MLOps frameworks and best practices, including CI/CD pipelines, model versioning, model registries, feature stores, and automated retraining workflows. Deploy, monitor, and optimize machine learning solutions using cloud platforms and containerized technologies such as Docker, Kubernetes, SageMaker, Vertex AI, or Azure ML. And the last one.
  • Collaborate across engineering and data teams to integrate scalable ML solutions into mission-critical applications while monitoring performance and addressing model drift. 

Here's what you need: 

  • Hands-on experience building, training, deploying, and maintaining machine learning models in production environments.
  • Strong proficiency in Python and experience with one or more machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost or Hugging Face.
  • Experience developing and maintaining end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring. Experience with MLOps practices and tools, including model versioning, CI/CD pipelines, model registries, feature stores, model monitoring, and automated retraining workflows. Experience deploying machine learning models using cloud native or containerized technologies such as Docker, Kubernetes, Amazon SageMaker, Google Vertex, AI or Azure Machine Learning.
  • Experience monitoring production machine learning systems, troubleshooting model performance issues, and addressing model drift.
  • Must be a U.S. Citizen (No Dual citizenship)

Bonus points if you have: 

  • Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required 

What Accenture Federal Services employees say

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