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Hugging Face Jobs in Baltimore, MD (NOW HIRING)

Tata Consultancy Service Limited

Engineer

Owings Mills, MD · On-site

$80K - $90K/yr

Experience with natural language processing (NLP) techniques and tools, such as SpaCy, NLTK, or Hugging Face. 6.Need to have knowledge of software development methodologies, such as Agile or Scrum. 7.

Tror AI for everyone

Data Scientist

Baltimore, MD · On-site

Familiarity with Hugging Face Transformers or similar libraries. * Experience in deploying models using APIs or cloud platforms (AWS, Azure, GCP). * Knowledge of MLOps practices and model lifecycle ...

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How much do hugging face jobs pay per hour?

As of Aug 11, 2026, the average hourly pay for hugging face in Baltimore, MD is $15.36, according to ZipRecruiter salary data. Most workers in this role earn between $12.88 and $18.17 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 Baltimore, MD? For Hugging Face jobs in Baltimore, MD, the most frequently searched job titles are:
What job categories do people searching Hugging Face jobs in Baltimore, MD look for? The top searched job categories for Hugging Face jobs in Baltimore, MD are:
What cities near Baltimore, MD are hiring for Hugging Face jobs? Cities near Baltimore, MD with the most Hugging Face job openings:
Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Baltimore, MD as of August 2026, with employment types broken down into 79% Full Time, 19% Part Time, and 2% Contract. Highlights an 90% Physical, 1% Hybrid, and 9% Remote job distribution, with an average salary of $31,946 per year, or $15.4 per hour.

Sr Lead AI Data Engineer - 100% onsite work and onsite interview

Unisoft Technology Inc

Silver Spring, MD • On-site

Other

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

Job description

        Bachelor''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''s degree in Computer Science, Data Science, Engineering, or a related field

     15+ years of experience in software or data engineering, with at least 4 years focused on ML systems or MLOps in a production environment.

        Demonstrated experience building or operating a shared/enterprise ML platform serving multiple teams or business units.

        Strong proficiency in Python and familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face).

        Hands-on experience with MLOps tooling: Kubeflow, MLflow, Airflow, or equivalent.

        Experience with cloud-native data and ML services on AWS.

        Working knowledge of LLMs, prompt engineering, and RAG architecture patterns.

        Experience with containerization and orchestration (Docker, Kubernetes).

        Strong understanding of data engineering concepts: pipelines, feature stores, data quality, and lineage.

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