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Machine Learning Teaching Assistant Jobs in Virginia

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Machine Learning Teaching Assistant information

What's the difference between a TA and a LSA?

In the context of a Machine Learning Teaching Assistant role, a TA typically assists with grading, student support, and course logistics, while an LSA (Learning Support Assistant) may focus more on providing additional instructional help or tutoring. The specific responsibilities can vary by institution, but TAs often have more formal teaching duties and may hold a graduate assistantship, whereas LSAs usually support learning in a more informal capacity.

How much does TA get paid?

The pay for a Machine Learning Teaching Assistant varies depending on the institution and location but typically ranges from $15 to $30 per hour. TAs often assist with grading, tutoring, and lab sessions, and some positions may offer stipends or hourly wages based on experience and responsibilities.

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.

Is ML a high paying job?

Machine Learning Teaching Assistants often earn competitive salaries, especially with relevant skills in programming, data analysis, and machine learning frameworks. Salaries can vary based on experience, location, and the level of education, but the field generally offers high earning potential compared to many entry-level roles.

What are Machine Learning Teaching Assistants?

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.

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 is an AI teaching assistant?

An AI teaching assistant is a software tool or program that uses artificial intelligence to support educational activities, such as answering student questions, grading assignments, or providing personalized learning recommendations. In the context of a machine learning teaching assistant role, it involves developing, maintaining, or improving AI systems that assist instructors and students in learning environments.
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Machine Learning Teaching Assistant jobs near you
Machine Learning Modeling and Simulation Engineer

Machine Learning Modeling and Simulation Engineer

Science Applications International Corporation

Chantilly, VA

Other

Posted 10 days ago

SAIC rating

7.8

Company rating: 7.8 out of 10

Based on 78 frontline employees who took The Breakroom Quiz

69th of 204 rated it services

Job description

SAIC has need for a Machine Learning Modeling and Simulation Engineer  to support a rapidly expanding Government Intelligence Community (IC) customer with cutting-edge programs within the National Reconnaissance Office (NRO) in Chantilly, VA.

Note:  The role offers a flexible work schedule, but we ask our team to be available for team meetings during core business hours (10:00 a.m. - 3:00 p.m.).

As the Machine Learning Modeling and Simulation Engineer, you will provide technical expertise across a variety of Machine Learning (ML) and Modeling and Simulation (M&S) topics, including developing and training ML models, designing simulation frameworks, conducting performance analyses, and applying data-driven approaches to solve complex problems. You will also assist with Systems Engineering topics (e.g., requirements, configuration management, readiness, verification and validation, etc.) to ensure seamless integration of ML capabilities within simulation environments. 

Job Duties to include:

  • Develop and maintain physics-based simulation models of spacecraft systems, including structures, sensors, and mission environments.
  • Perform end-to-end performance modeling for satellite missions, integrating sensor, orbital, and environmental models.
  • Conduct sensor phenomenology studies, including optical, infrared, or radar modeling for detection, tracking, and signature analysis.
  • Perform orbital mechanics modeling including orbit determination, orbital maneuvering, and spacecraft flight dynamics.
  • Use scripting languages (Python, MATLAB, or similar) to automate workflows, perform data analysis, and interface between simulation tools.
  • Apply Artificial Intelligence/Machine Learning (AI/ML) techniques (e.g., supervised/unsupervised learning, reinforcement learning, predictive modeling) to enhance simulation fidelity and performance.
  • Develop AI/ML models to analyze and predict satellite system behaviors, performance metrics, and mission outcomes based on simulation data.
  • Design and implement algorithms for anomaly detection, predictive maintenance, and optimization of satellite operations.
  • Use statistical and machine learning techniques to analyze data, identify patterns, and uncover insights relevant to satellite systems.
  • Integrate AI/ML models into existing simulation frameworks and tools to enhance their capabilities.
  • Provide value-added judgment and offer strategic recommendations to the customer on program objectives, advanced technologies, and system enhancements. 
  • Produce highly detailed, practical, and consistent deliverables that align with the organization's mission and objectives, with a focus on innovation and cutting-edge solutions in machine learning and simulation. 
SAIC is a premier mission integrator focused on advancing the power of technology and innovation to serve and protect our world. Our robust portfolio of offerings across the defense, space, intelligence, and civilian markets includes secure high-end solutions in mission IT, enterprise IT, engineering services, and professional services. We integrate emerging technology, rapidly and securely, into mission critical operations that modernize and enable critical national imperatives.

We are approximately 23,000 strong; driven by mission, united by purpose, and inspired by opportunities. SAIC is an Equal Opportunity Employer. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $7.3 billion. For more information, visit saic.com. For ongoing news, please visit our newsroom.

Required Education and Experience:

  • Bachelor's Aerospace Engineering, Mechanical Engineering, Physics, and five (5) years or more experience; Masters and three (3) years or more experience; PhD and 0 years related experience. 
  • Active Top Secret/SCI w/Poly Clearance.
  • 3+ years of experience in modeling and simulation for aerospace or space systems.
  • Strong understanding of sensor phenomenology --such as optical, infrared, or radar systems --and associated modeling methods.
  • Intermediate Python programming experience, demonstrated through hands-on experience with tasks such as data manipulation, automation, and development of Python-based solutions. Experience with libraries such as NumPy, SciPy, pandas, and matplotlib is beneficial.
  • Ability to communicate technical results clearly in written and verbal formats.

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