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Machine Learning Teaching Assistant Jobs in Lockport, IL

AppFolio

Sr. Machine Learning Engineer

Chicago, IL · On-site

$107K - $147K/yr

... Assistant (copilot), Flows (AI Agentic workflows) and Performers (autonomous AI Agents). Realm-X ... Who We Are Looking For We're hiring a Senior Machine Learning Engineer to design and ship the next ...

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Machine Learning Teaching Assistant information

See Lockport, IL salary details

$13

$18

$23

How much do machine learning teaching assistant jobs pay per hour?

As of Aug 20, 2026, the average hourly pay for machine learning teaching assistant in Lockport, IL is $18.11, according to ZipRecruiter salary data. Most workers in this role earn between $16.39 and $19.28 per hour, depending on experience, location, and employer.

What is a machine learning teaching assistant?

Machine Learning Teaching Assistants are individuals, often graduate students or knowledgeable undergraduates, who assist professors or instructors in teaching machine learning courses. Their responsibilities typically include helping students understand course material, grading assignments, holding office hours, and sometimes leading discussion or lab sessions. They act as a bridge between students and instructors, offering support for both theoretical concepts and practical implementation. By providing guidance and feedback, they help ensure students gain a solid understanding of machine learning principles and applications.

How does a machine learning teaching assistant typically collaborate with professors and students during a course?

As a Machine Learning Teaching Assistant, you will work closely with professors to develop and grade assignments, clarify course concepts, and facilitate discussions in lectures or lab sessions. You often serve as a bridge between students and faculty, providing guidance on programming tasks, troubleshooting code, and offering feedback on projects. Regular office hours and online forums are common venues for this support, making strong communication skills and a solid grasp of machine learning fundamentals essential. This collaborative environment helps you deepen your expertise while supporting student learning.

What are the key skills and qualifications needed to thrive as a machine learning teaching assistant, and why are they important?

To thrive as a Machine Learning Teaching Assistant, you need a solid foundation in machine learning concepts, programming (often Python), and relevant coursework or a degree in computer science or a related field. Familiarity with tools like Jupyter Notebooks, TensorFlow, PyTorch, and version control systems is commonly expected. Strong communication, patience, and organizational skills help you effectively support students and collaborate with instructors. These abilities ensure you can explain complex topics clearly, assist students efficiently, and contribute to a positive learning environment.

What job categories do people searching Machine Learning Teaching Assistant jobs in Lockport, IL look for?

The top searched job categories for Machine Learning Teaching Assistant jobs in Lockport, IL are:

What cities near Lockport, IL are hiring for Machine Learning Teaching Assistant jobs?

Cities near Lockport, IL with the most Machine Learning Teaching Assistant job openings:

Machine Learning Teaching Assistant jobs near you
Infographic showing various Machine Learning Teaching Assistant job openings in Lockport, IL as of August 2026, with employment types broken down into 1% As Needed, 69% Full Time, 25% Part Time, 3% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $37,661 per year, or $18.1 per hour.

Postbaccalaureate Appointee - Machine Learning for Viral Glycosylation Prediction

Argonne National Laboratory

Lemont, IL • On-site

Full-time

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

Job description

The Computing, Environment, and Life Sciences (CELS) directorate at Argonne National Laboratory is seeking a Post-Bachelor Appointee to contribute to research at the intersection of artificial intelligence, computational biology, and high-performance computing.
  • The successful candidate will join an interdisciplinary team developing machine learning approaches to understand glycosylation patterns across viral proteins, supporting research that advances computational methods for pathogen characterization, vaccine design, and therapeutic discovery.
  • Working under the guidance of experienced computational scientists, the appointee will assist in the development, implementation, validation, and evaluation of machine learning models for predicting glycosylation sites and glycan occupancy in viral proteins.
  • The position offers an opportunity to develop technical expertise in machine learning, computational biology, scalable software development, and scientific computing while gaining experience in a collaborative national laboratory research environment.

In this role, you can expect to:
  • Assist in the development, implementation, and evaluation of machine learning models for predicting glycosylation sites and glycosylation patterns in viral proteins.
  • Support the design and implementation of graph neural network (GNN) models and other deep learning approaches for learning sequence- and structure-based representations of viral proteins.
  • Collect, curate, preprocess, and integrate biological sequence, structural, and experimental datasets used for model development and benchmarking.
  • Develop software tools and computational workflows using modern machine learning frameworks such as PyTorch, PyTorch Geometric, TensorFlow, or related libraries.
  • Conduct model training, validation, benchmarking, and performance analysis using appropriate statistical and computational evaluation methods.
  • Assist in deploying and optimizing machine learning workflows on Argonne's high-performance computing systems.
  • Document software, datasets, computational workflows, and experimental results to promote reproducibility and maintainability.
  • Collaborate with computational scientists, biologists, and software engineers to interpret model predictions and improve computational methods.
  • Prepare technical reports, presentations, and documentation summarizing research progress and computational results.
  • Contribute to manuscripts, conference presentations, software releases, and other research dissemination activities as appropriate.
  • Participate in project meetings, technical discussions, and collaborative research activities across multidisciplinary teams.
  • Perform additional research and technical duties assigned by the supervisor in support of project objectives.

Expected Outcomes:
  • Success in this position will be demonstrated through:
  • Development of reproducible computational workflows supporting machine learning research on viral glycosylation.
  • Successful implementation and evaluation of machine learning models under the guidance of project scientists.
  • Contribution to scalable software and computational tools supporting ongoing research activities.
  • Effective collaboration within multidisciplinary teams.
  • Preparation of high-quality technical documentation, reports, and research presentations.
  • Growth in technical and research capabilities that prepare the appointee for graduate study or advanced research positions.

Position Requirements
Required Qualifications:
  • Recently completed Bachelor's degree in Computer Science, Bioinformatics, Computational Biology, Data Science, Biomedical Engineering, Applied Mathematics, or a related STEM discipline.
  • Experience programming in Python or a similar scientific programming language.
  • Basic knowledge of machine learning or deep learning methods.
  • Familiarity with one or more machine learning frameworks such as PyTorch, TensorFlow, or JAX.
  • Experience analyzing scientific or biological datasets through coursework, research projects, or internships.
  • Strong analytical and problem-solving skills.
  • Excellent written and verbal communication skills.
  • Demonstrated ability to work effectively both independently and as part of an interdisciplinary research team.
  • Ability to model Argonne's core values of impact, safety, respect, teamwork, ang integrity.

Preferred Qualifications:
  • Undergraduate research experience in machine learning, computational biology, bioinformatics, or related fields.
  • Experience with graph neural networks or representation learning.
  • Familiarity with protein sequence analysis, structural biology, glycobiology, or bioinformatics.
  • Experience using Linux environments, Git, and software development best practices.
  • Exposure to GPU computing, high-performance computing, or cloud computing environments.
  • Experience presenting research findings or contributing to scientific publications or open-source software projects.

Job Family
Temporary
Job Profile
Postbaccalaureate Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
The expected hiring range for this position is $58,656.00-$92,273.00.
Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.
Click here to view Argonne employee benefits!
As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.
All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.

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