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Hugging Face Jobs in Wisconsin (NOW HIRING)

Paradigm

WI · On-site

$125 - $150/hr

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)

Revature LLC

WI · On-site

$100 - $125/hr

Proficiency in Python and AI libraries such as PyTorch, TensorFlow, and Hugging Face Transformers. * Strong experience with LLMs, prompt engineering, and fine‑tuning. * Practical understanding of ...

Direct Supply, Inc.

AI Solutions Architect

Milwaukee, WI · On-site

$62 - $81.75/hr

Familiarity with state-of-the-art AI frameworks and LLMs, such as LangChain, Hugging Face, or OpenAI APIs. * Experience working directly with customers or cross-functional leadership teams.

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How much do hugging face jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for hugging face in Wisconsin is $15.60, according to ZipRecruiter salary data. Most workers in this role earn between $13.12 and $18.46 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.

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For Hugging Face jobs in Wisconsin, the most frequently searched job titles are:

What job categories do people searching Hugging Face jobs in Wisconsin look for?

The top searched job categories for Hugging Face jobs in Wisconsin are:

Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Wisconsin as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution, with an average salary of $32,451 per year, or $15.6 per hour.

Senior Applied ML Engineer

WI • On-site

Paradigm
See more jobs at Paradigmexternal

$125 - $150/hr

Other

Re-posted 4 days ago

Job description

Paradigm is a software company transforming the way that the residential, construction & building product industries operate across the globe. We are looking for a Senior Applied ML Engineer to be part of revolutionizing these industries.

We are looking for a Senior Applied ML Engineer to design, implement, and scale machine learning systems that power next-generation construction and digital twin solutions. You will apply advanced ML techniques—ranging from computer vision to large language models—to automate critical workflows such as blueprint understanding, 3D model generation, and materials forecasting. This role blends research, engineering, and domain expertise to deliver practical, production-ready AI systems that transform how homes are designed, estimated, and built.

What You Will Do
  • Develop and optimize CNN and LLM-powered models for computer vision, document extraction, and automated construction workflows.
  • Prototype, fine-tune, and assess models for NLP tasks such as classification, entity recognition, and summarization of construction data.
  • Build scalable ML pipelines and backend services that integrate into production-grade agents and digital platforms.
  • Drive the end-to-end ML lifecycle: from experimentation and training, to deployment, monitoring, and continuous improvement.
  • Integrate retrieval, ML, and rules-based methods to deliver reliable, explainable, and supportable features.
  • Collaborate closely with product managers, software engineers, and construction domain experts to solve real-world challenges with measurable business impact.
What You Need to Succeed
  • Bachelor’s or Master’s degree in Computer Science, Machine Learning, or related field.
  • 5+ years of experience designing and deploying applied ML systems at scale.
  • Experience with computer vision (CNNs, object detection, segmentation) and natural language processing (LLMs, embeddings, transformers).
  • 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).
  • Experience with APIs, CI/CD pipelines, cloud platforms (AWS/Azure/GCP).
  • Ability to clearly communicate technical concepts to both engineers and non-technical stakeholders.
  • Experience applying ML in construction, CAD/BIM, architecture, or digital twin platforms is preferred.
  • Familiarity with graph-based retrieval, RAG pipelines, or multimodal ML is preferred.
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