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Hourly Remote Machine Learning Engineer Jobs in American Fork, UT

... machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format, offering both remote and in-person opportunities (such as device ...

... machine learning models, including data labeling, content evaluation, and user-based testing. Projects may vary in scope and format, offering both remote and in-person opportunities (such as device ...

Lead Data Scientist

Draper, UT · On-site +1

$144K - $250K/yr

Machine Learning * Python (Programming Language) * R Statistics * Statistical Analysis * Statistics ... Normal office environment. (Remote or Hybrid), 3 to 4 days per month are required in office if ...

Showing results 21-40

Hourly Remote Machine Learning Engineer information

See American Fork, UT salary details

$22.8K

$38.1K

$78.7K

How much do hourly remote machine learning engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for hourly remote machine learning engineer in American Fork, UT is $38,065.00, according to ZipRecruiter salary data. Most workers in this role earn between $29,100.00 and $41,100.00 per year, depending on experience, location, and employer.

What does an hourly remote machine learning engineer do?

An Hourly Remote Machine Learning Engineer is a professional who develops and implements machine learning models and algorithms for clients or employers on an hourly contract basis, all while working from a remote location. Their responsibilities typically include data preprocessing, model selection, training, testing, and deployment. They collaborate with teams via online tools, manage their own schedules, and deliver results according to project requirements. This role allows for flexibility and the opportunity to work on diverse projects across different industries.

What are some common challenges faced by hourly remote machine learning engineers, and how can they be addressed?

Hourly remote machine learning engineers often encounter challenges such as managing time effectively across multiple projects, ensuring clear communication with distributed teams, and accessing necessary data or computing resources remotely. Building strong routines for regular check-ins and using collaborative tools can help maintain alignment with project goals. Additionally, proactively clarifying expectations and deliverables with clients or team leads can minimize misunderstandings and improve productivity in a remote, hourly environment.

What are the key skills and qualifications needed to thrive as an hourly remote machine learning engineer, and why are they important?

To thrive as an Hourly Remote Machine Learning Engineer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and experience with data preprocessing, typically supported by a relevant degree or equivalent experience. Familiarity with tools and frameworks such as TensorFlow, PyTorch, scikit-learn, cloud platforms (e.g., AWS, GCP), and version control systems like Git is essential. Excellent time management, self-motivation, and clear communication skills help you collaborate effectively across distributed teams and manage project-based work. These skills and qualities are vital for delivering high-quality results independently, meeting deadlines, and adapting to the dynamic needs of remote projects.

What are popular job titles related to Hourly Remote Machine Learning Engineer jobs in American Fork, UT?

For Hourly Remote Machine Learning Engineer jobs in American Fork, UT, the most frequently searched job titles are:

What job categories do people searching Hourly Remote Machine Learning Engineer jobs in American Fork, UT look for?

The top searched job categories for Hourly Remote Machine Learning Engineer jobs in American Fork, UT are:

What cities near American Fork, UT are hiring for Hourly Remote Machine Learning Engineer jobs?

Cities near American Fork, UT with the most Hourly Remote Machine Learning Engineer job openings:

Infographic showing various Hourly Remote Machine Learning Engineer job openings in American Fork, UT as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 75% Full Time, 22% Part Time, and 1% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $38,065 per year, or $18.3 per hour.

R&D Data Scientist

Salt Lake City, UT • On-site, Remote

Hexcel Corporation
Aerospace Product and Parts Manufacturing • 5 - 10K employees

Full-time

This job post has expired today. Applications are no longer accepted.


Key responsibilities

  • Lead the design, development, validation, and deployment of digital twins for manufacturing processes, material systems, and product development programs.

  • Develop hybrid modeling approaches that combine physics-based models, first-principles engineering, machine learning, and statistical methodologies.

  • Integrate sensor, process, quality, and operational data into digital twin frameworks to improve predictive capabilities and decision-making.


Hexcel rating

7.9

Company rating: 7.9 out of 10

Based on 20 frontline employees who took The Breakroom Quiz


Job description

With our strong investment in research and development and our culture of continuous improvement, Hexcel is the industry leader in the manufacturing of advance composite materials, including carbon fiber, woven reinforcements, resins, prepregs, honeycombs and engineered core and composite structures. We invite you to join the Hexcel team at various manufacturing sites, sales offices and R&D centers around the globe. Become a part of the "strength within."

Hexcel is currently seeking aResearch and Development Data Scientistfor ourSalt Lake City, UT, USA. This person will lead the development and deployment of digital twin technologies that accelerate material, process, and product innovation.This role will focus on building virtual representations of manufacturing processes, materials, and products using advanced analytics, machine learning, physics-based modeling, and AI. The successful candidate will bridge physical experimentation and digital simulation to improve process performance, reduce development cycles, and support next-generation composite material technologies.

The selected individual will be responsible for but not limited to the following obligations:

  • Lead the design, development, validation, and deployment of digital twins for manufacturing processes, material systems, and product development programs.
  • Develop hybrid modeling approaches that combine physics-based models, first-principles engineering, machine learning, and statistical methodologies.
  • Integrate sensor, process, quality, and operational data into digital twin frameworks to improve predictive capabilities and decision-making.
  • Design and implement advanced AI, machine learning, and analytics solutions that enhance model accuracy, process control, and product performance.
  • Collaborate with scientists, engineers, technicians, and manufacturing teams to translate physical system behavior into scalable digital twin architectures.
  • Support experimentation strategies, including Design of Experiments (DOE), active learning, and closed-loop optimization to continuously improve digital twin performance.
  • Develop simulation environments for evaluating process changes, material innovations, and manufacturing outcomes before physical implementation.
  • Establish model governance practices including validation, uncertainty quantification, monitoring, and lifecycle management of digital twin applications.
  • Communicate digital twin insights and recommendations to technical and business stakeholders to drive data-driven innovation and operational excellence.

Required Qualifications

  • Master's degree in data science, Materials Science, Chemical Engineering, Mechanical Engineering, Applied Mathematics, Physics, or a related technical discipline.
  • Minimum 2+ years of experience developing predictive models, digital twins, simulation tools, or advanced analytics solutions in an industrial environment.
  • Demonstrated experience creating and validating digital twin or virtual process models using machine learning, statistical modeling, physics-based modeling, or hybrid approaches.
  • Experience modeling complex physical, chemical, or manufacturing systems.
  • Strong analytical and problem-solving skills with the ability to connect physical processes to data-driven modeling techniques.
  • Proficiency in Python, R, or similar programming languages and associated machine learning ecosystems.
  • Experience with cloud-based data and AI platforms, preferably Microsoft Azure.
  • Strong communication skills with the ability to explain complex modeling concepts to multidisciplinary teams.
  • Eligible candidates must be a U.S. citizen.

Preferred Qualifications

  • Experience with Digital Twin platforms, Industrial IoT architectures, or digital thread initiatives.
  • Experience with Azure Digital Twins, Azure Machine Learning, Azure IoT, Microsoft Fabric, Synapse, or related Microsoft technologies.
  • Experience working with manufacturing data, process optimization, materials development, or composite materials.
  • Familiarity with ML Ops, model deployment, model governance, and continuous monitoring frameworks.
  • Experience utilizing large language models (LLMs), generative AI, or AI agents to support engineering and scientific workflows.
  • Experience applying AI and optimization techniques to material formulation or chemical composition development

Eligible candidates must be a: U.S. citizen, U.S. national, person lawfully admitted for permanent residence, temporary resident under sections 210(a) or 245(A) of the Immigration and Nationality Act, person admitted in refugee status, or person granted asylum. Hexcel (NYSE: HXL) is a global leader in advanced composites technology, a leading producer of carbon fiber, and the world leader in honeycomb manufacturing for the commercial aerospace industry.

Hexcel is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, physical or mental disability, status as a protected veteran, or any other protected class.


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