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Hugging Face Jobs in Iowa (NOW HIRING)

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$8

$14

$19

How much do hugging face jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for hugging face in Iowa is $14.52, according to ZipRecruiter salary data. Most workers in this role earn between $12.21 and $17.16 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 Iowa? For Hugging Face jobs in Iowa, the most frequently searched job titles are:
What job categories do people searching Hugging Face jobs in Iowa look for? The top searched job categories for Hugging Face jobs in Iowa are:
Hugging Face jobs near you
Infographic showing various Hugging Face job openings in Iowa as of July 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution, with an average salary of $30,198 per year, or $14.5 per hour.

AI Developer

American technologies consulting

West Des Moines, IA • On-site

Contractor

Re-posted 15 days ago

Job description

Job Overview

We are seeking a skilled AI Developer to design, build, and deploy autonomous AI agents from scratch. This role involves creating intelligent systems that can perceive environments, make decisions, and execute actions in real-world or simulated scenarios. You will leverage machine learning, Python, and specialized frameworks like LangChain and LangGraph to develop scalable AI agents for applications such as automation, robotics, virtual assistants, or multi-agent simulations.

Key Responsibilities
  • Architect and implement AI agents from the ground up using frameworks such as LangChain for chaining LLMs and tools, and LangGraph for stateful, graph-based agent workflows, including perception modules (e.g., using computer vision or NLP), decision-making logic (e.g., via reinforcement learning or planning algorithms), and action execution components.
  • Develop and train machine learning models using frameworks like TensorFlow, PyTorch, or Scikit-learn to enable agent learning and adaptation, integrating with LangChain/LangGraph for advanced agent orchestration.
  • Integrate AI agents with external systems, APIs, databases, and environments (e.g., simulation tools like OpenAI Gym or real-world interfaces), ensuring seamless tool usage and memory management via LangChain components.
  • Optimize agents for performance, scalability, and robustness, including handling edge cases, ethical considerations, and safety protocols within graph-structured agent designs.
  • Collaborate with cross-functional teams (e.g., data scientists, software engineers) to iterate on agent designs based on feedback and testing.
  • Conduct experiments, simulations, and evaluations to refine agent behaviors and ensure reliability in production.
  • Document code, architectures, and methodologies for reproducibility and team knowledge sharing.
  • Stay current with advancements in AI agent technologies, such as large language models (LLMs), multi-agent systems, and emerging frameworks like LangChain and LangGraph.
Required Skills and Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
  • Proficiency in Python programming, with strong experience in ML libraries (e.g., TensorFlow, PyTorch, NumPy, Pandas) and agent-specific tools (e.g., LangChain, LangGraph, AutoGen, RLlib, Hugging Face Transformers).
  • Hands-on experience building AI agents from scratch using LangChain for tool integration and agent chains, LangGraph for multi-step reasoning and state management, including reinforcement learning, state machines, graph-based planning, or evolutionary algorithms.
  • Solid understanding of data structures, algorithms, software engineering principles, and version control (e.g., Git).
  • Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) for deploying agents, and tools like Docker/Kubernetes for containerization.
  • Strong problem-solving skills, with the ability to debug complex systems and work in agile, fast-paced environments.
  • Excellent communication skills to articulate technical designs and collaborate effectively.
Preferred Qualifications
  • Experience with specialized domains like natural language processing (NLP), computer vision, robotics (e.g., ROS), or game AI, integrated with LangChain/LangGraph.
  • Knowledge of big data tools (e.g., Spark, Hadoop) or databases (SQL/NoSQL) for handling large-scale agent data.
  • Prior work with multi-agent systems, ethical AI, or real-time applications using advanced frameworks.
  • Contributions to open-source AI projects or a portfolio demonstrating agent-building expertise (e.g., GitHub repos showcasing LangChain/LangGraph implementations).