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Hugging Face Jobs in Seattle, WA (NOW HIRING)

Paradigm

Senior Applied ML Engineer

Seattle, WA · Remote

$125K - $183K/yr

Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face). * Experience with ML Ops platforms and deploying ML systems into production (MLflow, Kubeflow or equivalent)

Paradigm

Senior Applied ML Engineer

Seattle, WA · On-site

$123K - $170K/yr

... Hugging Face). · Experience with ML Ops platforms and deploying ML systems into production (MLflow, Kubeflow or equivalent). · Experience with APIs, CI/CD pipelines, cloud platforms (AWS/Azure/GCP ...

Intac Solution

[CLOSED] Network Tester

Bellevue, WA

$34 - $36/hr

Proficiency in AI/ML platforms and APIs (e.g., OpenAI, TensorFlow, PyTorch, Hugging Face, Azure AI Studio). * Detect and track software defects and inconsistencies; analyzing the testing results and ...

HCLTech

Gen AI Architect

Redmond, WA · On-site

MCP Server, Agent 2 Agent Communication, Hugging Face Transformers, OpenAI APIs, and diffusion models (for image generation). * Working knowledge of Azure platform and PaaS services * Ability to work ...

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How much do hugging face jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for hugging face in Seattle, WA is $17.59, according to ZipRecruiter salary data. Most workers in this role earn between $14.76 and $20.77 per hour, depending on experience, location, and employer.

Can you make money on Hugging Face?

Hugging Face is a platform that offers opportunities for data scientists, machine learning engineers, and developers to monetize their skills through jobs, freelance projects, or contributing to open-source models. Earning potential depends on the type of work, experience, and whether you are employed directly or working independently. Building a strong portfolio and expertise in NLP and AI tools can increase income opportunities on the platform.

Which 3 jobs will survive AI?

Jobs that require complex human interaction, creativity, and critical thinking, such as healthcare professionals, educators, and skilled tradespeople, are likely to persist despite AI advancements. These roles often involve emotional intelligence, nuanced judgment, and hands-on skills that are difficult for AI to replicate. Continuous learning and adaptability remain important for job security in an evolving technological landscape.

What are Hugging Face jobs?

Hugging Face jobs refer to employment opportunities at the company focused on developing and maintaining open-source machine learning tools, especially in natural language processing. Roles may include software engineering, research, data science, and product management, often requiring skills in Python, deep learning frameworks, and collaboration in a tech environment.

How much do Hugging Face engineers make?

Hugging Face engineers' salaries vary based on experience, role, and location, but generally range from $100,000 to $180,000 annually. Senior positions and specialized roles in machine learning or software engineering tend to offer higher compensation, often including stock options and benefits.

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 Seattle, WA? For Hugging Face jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Hugging Face jobs in Seattle, WA look for? The top searched job categories for Hugging Face jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Hugging Face jobs? Cities near Seattle, WA with the most Hugging Face job openings:
Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Seattle, WA as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $36,588 per year, or $17.6 per hour.
Gen AI Architect

Gen AI Architect

Tata Consultancy Services

Seattle, WA • On-site

Full-time

Re-posted 12 days ago

Tata Consultancy Services rating

6.5

Company rating: 6.5 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

159th of 210 rated it services

Job description

Job Summary:
Tata Consultancy Services is seeking a Gen AI Architect to lead innovative machine learning solutions. The role involves developing GenAI applications, educating teams on best practices, and driving the delivery of reliable solutions while championing teamwork and effective communication across various levels of management.
Responsibilities:
• Be an expert source on machine learning to drive delivery of new and innovative solutions.
• Propose creative solutions to approach business solutions with emerging technologies.
• Prototype new ways of applying technologies for solving business problems.
• Educate others so that they can demonstrate the innovative methods for achieving outcomes.
• Build and maintain machine learning principles, best practices, and code accelerators.
• Conduct external research and internal experimentation for machine learning techniques.
• Champion solution delivery behaviors and approaches from software engineers that accelerate delivery of reliable solutions and create a culture of teamwork.
• Analyze and communicate strategy, status, and product roadmaps to multiple audiences, including all levels of management.
• GenAI Application Development Expertise
• Programming Languages: Python
• Development Tools: LangChain, LlamaIndex, LangFlow, Langgraph, LangSmith, Flowise
• Techniques: RAG Techniques
• Databases: Vector Databases (Pinecone, Weaviate, Qdrant)
• Additional Technologies: Knowledge Graphs, FastAPI, Streamlit, Gradio
• Domain Model Fine-Tuning Capabilities
• Languages & Libraries: Python, Data Engineering, OSS LLMs (Llama2, Mixtral, GPT-Neo, GPT-J)
• Tokenization & Frameworks: Tokenizers (SentencePiece, Hugging Face Tokenizers), Fine-tuning Frameworks (Hugging Face Transformers, PyTorch Lightning)
• Datasets: HuggingFace Datasets, TensorFlow Datasets
• LLMOps Proficiency
• Infrastructure & CI/CD: DevOps, Kubernetes, Docker, Git, Jenkins, GitLab, GitHub Actions
• Monitoring & Management: Ray, SeldonCore, MLFlow, MLServer, Triton, BentoML, Prometheus, Grafana
• Data Engineering for AI Applications
• Data Processing & Management: Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), Delta Lake
• Workflow Automation: Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, Great Expectations
• Data Catalogs: Amundsen, Collibra, Alation
• AI-Ready Cybersecurity Knowledge
• Threat Modeling & Security: AI-Specific Threat Modeling Tools, Secure ML Pipeline Tools, API Security Tools
• Monitoring & Prevention: AI Security Monitoring Tools, Prompt Injection Prevention Libraries, Adversarial Example Detection Libraries
• GenAI Guardrails and Ethics
• Ethics & Fairness: AI Ethics Frameworks and Tools, Bias Detection and Mitigation Tools, Fairness Metrics Libraries
• Privacy & Security: Privacy-Preserving Machine Learning Libraries, Robustness and Security Tools
• Transparency & Governance: Model Interpretability Libraries, AI Governance Frameworks and Tools
• Lead a team of junior developers
• Ability to translate requirements into understandable technical design.
• Task effort estimation and distribution among team members.
• Excellent communication skills, and stakeholder management.
• Ability to work with Cross functional teams and business users.
Qualifications:
Required:
• Be an expert source on machine learning to drive delivery of new and innovative solutions.
• Propose creative solutions to approach business solutions with emerging technologies.
• Prototype new ways of applying technologies for solving business problems.
• Educate others so that they can demonstrate the innovative methods for achieving outcomes.
• Build and maintain machine learning principles, best practices, and code accelerators.
• Conduct external research and internal experimentation for machine learning techniques.
• Champion solution delivery behaviors and approaches from software engineers that accelerate delivery of reliable solutions and create a culture of teamwork.
• Analyze and communicate strategy, status, and product roadmaps to multiple audiences, including all levels of management.
• GenAI Application Development Expertise
• Programming Languages: Python
• Development Tools: LangChain, LlamaIndex, LangFlow, Langgraph, LangSmith, Flowise
• Techniques: RAG Techniques
• Databases: Vector Databases (Pinecone, Weaviate, Qdrant)
• Additional Technologies: Knowledge Graphs, FastAPI, Streamlit, Gradio
• Domain Model Fine-Tuning Capabilities
• Languages & Libraries: Python, Data Engineering, OSS LLMs (Llama2, Mixtral, GPT-Neo, GPT-J)
• Tokenization & Frameworks: Tokenizers (SentencePiece, Hugging Face Tokenizers), Fine-tuning Frameworks (Hugging Face Transformers, PyTorch Lightning)
• Datasets: HuggingFace Datasets, TensorFlow Datasets
• LLMOps Proficiency
• Infrastructure & CI/CD: DevOps, Kubernetes, Docker, Git, Jenkins, GitLab, GitHub Actions
• Monitoring & Management: Ray, SeldonCore, MLFlow, MLServer, Triton, BentoML, Prometheus, Grafana
• Data Engineering for AI Applications
• Data Processing & Management: Python, Apache Spark, Apache Kafka, AWS S3, Azure Data Lake Storage (ADLS), Delta Lake
• Workflow Automation: Apache Airflow, dbt, Apache NiFi, Fivetran, Airbyte, Great Expectations
• Data Catalogs: Amundsen, Collibra, Alation
• AI-Ready Cybersecurity Knowledge
• Threat Modeling & Security: AI-Specific Threat Modeling Tools, Secure ML Pipeline Tools, API Security Tools
• Monitoring & Prevention: AI Security Monitoring Tools, Prompt Injection Prevention Libraries, Adversarial Example Detection Libraries
• GenAI Guardrails and Ethics
• Ethics & Fairness: AI Ethics Frameworks and Tools, Bias Detection and Mitigation Tools, Fairness Metrics Libraries
• Privacy & Security: Privacy-Preserving Machine Learning Libraries, Robustness and Security Tools
• Transparency & Governance: Model Interpretability Libraries, AI Governance Frameworks and Tools
• Bachelor’s degree in computer science, Data Science, or a related field; master’s degree preferred.
• 5+ years of professional and/or postgraduate academic research experience in software engineering.
• 4+ year of experience designing and developing machine learning solutions.
• 3+ years of experience with cloud native engineering, AWS, Azure, Google.
• Lead a team of junior developers
• Ability to translate requirements into understandable technical design.
• Task effort estimation and distribution among team members.
• Excellent communication skills, and stakeholder management.
• Ability to work with Cross functional teams and business users.
Preferred:
• Preferred experience in SAP / Salesforce or Oracle programs.
Company:
Tata Consultancy Services is a business solutions company that specializes on information technology services and consulting. It is a sub-organization of Tata Group. Founded in 1968, the company is headquartered in Mumbai, IND, with a team of 10001+ employees. The company is currently Late Stage.

What Tata Consultancy Services employees say

Pay

Benefits

Hours and flexibility

Workplace

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About Tata Consultancy Services

Sourced by ZipRecruiter

Tata Consultancy Services is an IT services, consulting and business solutions organization that delivers real results to global business, ensuring a level of certainty no other firm can match. TCS offers a consulting-led, integrated portfolio of IT, BPO, infrastructure, engineering, and assurance services. This is delivered through its unique Global Network Delivery Model™, recognized as the benchmark of excellence in software development. TCS delivers a level of certainty that no other firm can match--to our clients and to our employees. Come join us and experience certainty in your career. TCS a global Consulting and IT Services firm that is ranked in the top quartile by industry analysts. Our 2021 fiscal revenues topped $25 B and our market capitalization is over $170+B, yet we have a deep and large history of philanthropy and corporate social responsibility. Now approaching 600K of the best IT professionals and consultants, we are a trusted advisor, guiding our clients' enterprises through growth and transformation journeys - helping them to become agile, intelligent, automated and on the cloud. We are devoted to DEI and are recognized as a top employer and place to work.

Industry

It services

Company size

10,000+ Employees

Headquarters location

Edison, NJ, US

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