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Hugging Face Jobs in Raleigh, NC (NOW HIRING)

Senior AI Systems Engineer

Raleigh, NC · On-site +1

$92K - $126K/yr

Experience with AI/ML frameworks and tooling such as PyTorch, Hugging Face, or similar ecosystems. * Proficiency with DevOps and MLOps practices, including CI/CD pipelines, Git-based workflows ...

AI/ML Engineer

Durham, NC · Remote

$140K - $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

Durham, NC · Remote

$140K - $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

Raleigh, NC · Remote

$140K - $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

Raleigh, NC · Remote

$140K - $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 ...

Senior AI Systems Engineer

Raleigh, NC · On-site

$92K - $126K/yr

Experience with AI/ML frameworks and tooling such as PyTorch, Hugging Face, or similar ecosystems. * Proficiency with DevOps and MLOps practices, including CI/CD pipelines, Git-based workflows ...

AI Engineer

Cary, NC · On-site

$100K - $120K/yr

... Hugging Face) • Knowledge of MLOps tools (MLflow, Kubeflow, Airflow) • Experience with big data technologies (Spark, Databricks) • Exposure to computer vision or NLP use cases Roles ...

Experience in Python and the modern ML stack (PyTorch, Hugging Face, vLLM or equivalent) * Proven experience with agentic frameworks (LangGraph, AutoGen, CrewAI, or custom) and tool-use patterns ...

LangChain, LlamaIndex, Azure OpenAI, Hugging Face. * Knowledge of reinforcement learning, computer vision, or multimodal AI. * Experience with prompt engineering or model evaluation frameworks.

AI/ML Tech Lead

Raleigh, NC · On-site

$140K - $180K/yr

Experience integrating with AI platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Hugging Face). Strong understanding of API design, event-driven systems, and cloud-native architectures. Excellent ...

AI/ML Tech Lead

Raleigh, NC · On-site

$140K - $180K/yr

Experience integrating with AI platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Hugging Face). Strong understanding of API design, event-driven systems, and cloud-native architectures. Excellent ...

Experience integrating with AI platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Hugging Face). Strong understanding of API design, event-driven systems, and cloud-native architectures. Excellent ...

AI/ML Lead Architect

Raleigh, NC · Hybrid

$53.75 - $73.75/hr

Experience integrating with AI platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Hugging Face). Strong understanding of API design, event-driven systems, and cloud-native architectures. Excellent ...

Gen AI / Agentic AI Lead

Raleigh, NC · On-site

$136K - $167K/yr

... Hugging Face Transformers, LangChain, PyTorch). • Hands-on experience with vector databases (FAISS, Pinecone, Weaviate, Azure AI Search). • Familiarity with cloud platforms and Gen AI services ...

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How much do hugging face jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for hugging face in Raleigh, NC is $15.03, according to ZipRecruiter salary data. Most workers in this role earn between $12.60 and $17.74 per hour, depending on experience, location, and employer.

What professions make 500,000 a year?

Professions that can earn $500,000 or more annually include senior roles such as CEOs, investment bankers, specialized surgeons, and successful entrepreneurs. High earnings often require extensive experience, advanced skills, and often involve leadership or highly specialized knowledge in their fields.

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 Raleigh, NC? For Hugging Face jobs in Raleigh, NC, the most frequently searched job titles are:
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What cities near Raleigh, NC are hiring for Hugging Face jobs? Cities near Raleigh, NC with the most Hugging Face job openings:
Infographic showing various Hugging Face job openings in Raleigh, NC as of May 2026, with employment types broken down into 31% Full Time, 65% Part Time, and 4% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $31,253 per year, or $15 per hour.

Full-time

Posted 16 days ago


Job description

Overview:
Description:
"Responsibilities
Advanced Data Solutions & Engineering
• Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems.
• Build and integrate cloud-native data pipelines using tools such as Snowflake, Airflow, and Vertex AI.
• Implement retrieval-augmented generation (RAG) pipelines and multimodal data solutions.
• Drive automation, observability, and performance optimization across AI and data workflows.
Innovation & Applied AI
• Lead initiatives to explore, validate, and scale emerging AI technologies.
• Translate research and prototypes into production-ready capabilities.
• Collaborate across teams to embed AI-driven insights and automation into business processes.
• Evaluate and shape next-generation AI trends, including agentic systems and autonomous workflows.
Technology Leadership & Best Practices
• Champion hands-on experimentation and rapid solution delivery while maintaining technical excellence.
• Define and promote engineering standards that balance agility, scalability, and governance.
• Collaborate with security, compliance, and governance partners to ensure responsible data and AI usage.
• Mentor engineers and architects in modern data and AI development practices.
Collaboration & Knowledge Sharing
• Act as a trusted advisor for business and technology leaders on data-driven innovation.
• Lead internal workshops and training sessions to accelerate AI adoption.
• Represent the organization in external forums, conferences, and publications focused on data and AI innovation.
Qualifications
• 10+ years of experience in enterprise data architecture or engineering, with a strong hands-on focus on AI and cloud-native data platforms.
• Proven experience in designing, implementing, and optimizing large-scale AI systems, including LLM-based, GenAI, and agentic AI applications.
• Expertise in Python, SQL, and modern data frameworks (e.g., PySpark, Airflow, Snowflake, LangChain, Hugging Face, Vertex AI, OpenAI)
• Strong background in data modelling, distributed systems, and cloud architecture (AWS, GCP, or Azure).
• Experience developing and deploying AI/ML/GenAI pipelines leveraging vector databases and RAG frameworks.
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
Preferred:
• Experience with agentic AI design patterns, including tool-use orchestration, autonomous workflow agents, or AI copilots.
• Proficiency in API design, microservices, and containerization (Docker, Kubernetes).
• Demonstrated ability to rapidly prototype new AI concepts and transition successful PoCs into production-grade systems. "
Role Description: GenAI, LLMs, RAG pipelines, vector databasesPython SQL (expert level)Cloud-native data platforms (Snowflake, Airflow, Vertex AI AWS Azure)Modern AI frameworks (LangChain, Hugging Face, PySpark)Data modeling cloud architectureBuilding AIML pipelines end-to-endHands-on prototyping and productionizing AI solutions
Competencies: Data Architecture and Modeling