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Hugging Face Jobs in Redmond, WA (NOW HIRING)

Gates Foundation

Senior AI Engineer (*3-Year LTE)

Seattle, WA · On-site

$118K - $163K/yr

... Hugging Face. • Harden successful prototypes into production: tests, observability, cost and latency monitoring, failure handling, and documentation that let other people trust and reuse them. • ...

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)

Intac Solution

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 ...

SpangleAI

AI/ML Scientist

Bellevue, WA · On-site

... Hugging Face, and libraries focused on GAI/LLM development • Familiarity with data warehouse and data pipeline technologies (e.g., Amazon Redshift, Google BigQuery, Snowflake, Apache Airflow) • ...

SpangleAI

AI/ML Scientist

Bellevue, WA · On-site

Proficiency in Python and machine learning frameworks such as PyTorch, TensorFlow, Hugging Face , and libraries focused on GAI/LLM development * Familiarity with data warehouse and data pipeline ...

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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 Redmond, WA is $17.31, according to ZipRecruiter salary data. Most workers in this role earn between $14.52 and $20.48 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 job categories do people searching Hugging Face jobs in Redmond, WA look for? The top searched job categories for Hugging Face jobs in Redmond, WA are:
What cities near Redmond, WA are hiring for Hugging Face jobs? Cities near Redmond, WA with the most Hugging Face job openings:
Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Redmond, WA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $36,007 per year, or $17.3 per hour.

AI/ML Technical Lead

Globenet Consulting Corp

Bellevue, WA • On-site

$150 - $230/hr

Other

Posted 6 days ago

Job description

Benefits:
  • Competitive salary
  • Opportunity for advancement
  • Training & development
Role: AI/ML Technical Lead Location: Fort Belvoir, VA 22060 Let’s Create Our Future Together at The AES Group! Position Overview

We are seeking an AI/ML Technical Lead to design, build, and deploy scalable machine learning models and AI-powered solutions. This role will collaborate with engineering, product, data, and business teams to transform complex data into practical, measurable solutions. The ideal candidate has strong technical leadership, problem-solving skills, production AI/ML experience, and expertise in Large Language Models.

Key Responsibilities
  • Lead the design, development, training, testing, and deployment of AI and machine learning models.
  • Build scalable ML pipelines for data processing, model training, validation, and production deployment.
  • Work with structured and unstructured data, including text, images, documents, and large datasets.
  • Collaborate with data engineers, software engineers, and product teams to integrate AI/ML capabilities into applications.
  • Evaluate and improve model accuracy, efficiency, reliability, scalability, and performance.
  • Develop predictive models, recommendation systems, NLP tools, automation workflows, and generative AI solutions.
  • Research and apply modern AI/ML tools, techniques, architectures, and best practices.
  • Monitor deployed models and address model drift, bias, data quality, and performance issues.
  • Document model architecture, assumptions, limitations, metrics, and technical decisions.
  • Promote responsible AI practices related to security, privacy, fairness, governance, and compliance.
  • Provide technical direction, code reviews, mentoring, and implementation guidance to engineering teams.
Required Qualifications
  • Active Secret security clearance or higher.
  • Bachelor’s degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related field.
  • Three or more years of experience in AI, machine learning, data science, or software engineering.
  • Strong Python programming skills.
  • Experience with PyTorch, TensorFlow, Scikit-learn, Keras, XGBoost, or similar frameworks.
  • Experience developing and deploying production machine learning models.
  • Strong knowledge of algorithms, feature engineering, statistical analysis, and model evaluation.
  • Experience processing large datasets using modern data tools.
  • Familiarity with APIs, cloud platforms, and software development practices.
  • Ability to communicate complex technical concepts to technical and non-technical stakeholders.
Preferred Qualifications
  • Master’s degree or PhD in a related field.
  • Experience with Generative AI, LLMs, NLP, computer vision, or deep learning.
  • Experience with Ask Sage, Hugging Face, LangChain, OpenAI APIs, Azure AI, AWS SageMaker, or Google Vertex AI.
  • Experience with MLflow, Kubeflow, Airflow, Docker, Kubernetes, and CI/CD pipelines.
  • Experience with SQL, Spark, Databricks, Snowflake, or cloud data warehouses.
  • Knowledge of AI governance, ethics, bias testing, security, and data privacy standards.
  • Experience deploying AI solutions in enterprise environments.
Technical Skills
  • Languages: Python, SQL, and R
  • ML Frameworks: PyTorch, TensorFlow, Scikit-learn, and XGBoost
  • Cloud Platforms: AWS, Microsoft Azure, or Google Cloud
  • MLOps: Docker, Kubernetes, MLflow, Airflow, and CI/CD
  • Data Tools: Pandas, NumPy, Spark, Snowflake, and Databricks
  • AI/LLM Tools: Ask Sage, Hugging Face, LangChain, OpenAI, and vector databases
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