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Volunteer Machine Learning Jobs in Washington (NOW HIRING)

Pcaob As

Chief Learning Officer

Washington, DC · On-site

$222.40 - $348.48/hr

Well-being and family resources** - Mental health and well-being resources, paid volunteer time ... machine learning, generative AI.* Design and lead an effort to provide scalable learning and ...

Pcaobus

Chief Learning Officer

Washington, DC · On-site

$248K/yr

Well-being and family resources - Mental health and well-being resources, paid volunteer time ... Strong understanding of AI concepts, including machine learning, generative AI, and related data ...

Amches, Inc.

2906 AI Engineer

Annapolis, MD · On-site

$110 - $170/hr

Develop, train, and evaluate a variety of machine learning models, ensuring they meet performance ... voluntary life insurance available #J-18808-Ljbffr

InterImage

2906 AI Engineer

Annapolis, MD · On-site

Develop, train, and evaluate a variety of machine learning models, ensuring they meet performance ... additional voluntary life insurance available #CJ Clearance Level: TS/SCI FSP Job Location:

Belay Technologies

Senior Software Engineer

Columbia, MD · On-site

$190K - $240K/yr

This position is ideal for an engineer who enjoys combining data science, machine learning, and ... volunteer program * 10% matching in 401(k) contributions vested on day one * $5,000 annual training ...

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How much do volunteer machine learning jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for volunteer machine learning in Washington is $21.67, according to ZipRecruiter salary data. Most workers in this role earn between $16.35 and $22.88 per hour, depending on experience, location, and employer.

What is a volunteer machine learning?

A Volunteer Machine Learning job involves contributing to machine learning projects without monetary compensation, often for nonprofits, open-source initiatives, or research. Volunteers may help with data preprocessing, model training, evaluation, or deployment. It’s a great opportunity to gain hands-on experience, collaborate with professionals, and apply ML skills to meaningful causes.

What types of projects or tasks can I expect to work on as a volunteer machine learning?

As a Volunteer Machine Learning contributor, you may work on a wide variety of tasks such as preparing and cleaning datasets, developing or enhancing machine learning models, conducting data analysis, and documenting results for open-source or nonprofit initiatives. Projects can range from building predictive models for social impact organizations to supporting data-driven research or assisting with educational outreach. You’ll typically collaborate remotely with other volunteers, data scientists, and project managers, often using shared code repositories and communication tools. This role provides valuable hands-on experience and networking opportunities within the growing field of machine learning.

What are the key skills and qualifications needed to thrive in the volunteer machine learning position?

To thrive as a Volunteer Machine Learning professional, you need a solid understanding of machine learning concepts, data analysis, and basic programming skills, typically demonstrated through coursework, personal projects, or relevant certifications. Familiarity with tools such as Python, TensorFlow, scikit-learn, and collaborative platforms like GitHub is often expected. Strong communication, collaboration, and adaptability are vital for contributing effectively within diverse volunteer teams. These skills and qualities enable volunteers to make meaningful technical contributions while supporting the goals of nonprofit or research-focused projects.

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NIST PREP Postdoc Associate in Process Modeling using Physically Informed Machine Learning

Southeastern Universities Research Association

Gaithersburg, MD • On-site

$84K - $92K/yr

Full-time

Re-posted 7 days ago

Job description

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest and thus requires that such institutions be the recipients of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
Research Title: Process Modeling using Physically Informed Machine Learning
The work will entail:
  • Designing and training physics-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements.
  • Implementing algorithms to assess the performance of PIML models.
  • Assessing uncertainty in the predictions of PIML models.
  • Developing systems for multiscale modeling of atomic layer deposition processes.
  • Developing software to implement the goals stated above (most likely in Python).
  • Disseminating results through posters/seminars and international meetings and meeting seminars.
  • Ensuring that all results, findings, data, software, etc. are correctly archived and transmitted through appropriate channels.

U.S. Citizen Preferred
Key responsibilities will include but are not limited to:
  • Algorithm development, implementation, and analysis
  • Analyze heterogeneous data sources.
  • Presenting results at internal meetings, and occasional meetings with external stakeholders.
  • Ensuring that results, protocols, software, and documentation have been archived or otherwise transmitted to the larger organization.

Qualifications
  • A Ph.D degree in Chemistry, Physics, Mathematics, Computer Science, Data Science, or a related field.
  • Significant course work in one or more of chemistry, physics, mathematics, statistics and/or computer science.
  • Familiarity with one or more chemical process modeling packages (e.g. Cantera, CHEMKIN).
  • Familiarity with one or more AI/ML software packages (e.g. Tensorflow or Pytorch).
  • Ability to program in a modern computational language (e.g. Python).
  • Strong oral and written communication skills.

Privacy Act StatementAuthority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate the administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated. By applying to a CHIPS-funded PREP opportunity, you also acknowledge that participation in the project requires signing a Non-Disclosure Agreement (NDA) prior to beginning any work.
SURA is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
PREP0003547

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