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Part Time Nvidia Machine Learning Jobs (NOW HIRING)

Booz Allen Hamilton, Inc.

Autonomy SME, Lead

Washington, DC · On-site

$116K - $152K/yr

Design and train machine learning models for perception, object detection, tracking, and ... Experience building and deploying AI models on edge hardware, such as NVIDIA Jetson, GPUs, and ...

New York University

RESEARCH SCHOLAR

New York, NY · On-site

$27/hr

Develop and evaluate models in machine learning and reinforcement learning * Publish papers in top ... Nvidia Isaac Gym, Simulator, as exemplified by a strong publication record. Expected start date and ...

Uottawa

APTPUO Winter 2027- MIA5100- (ONLINE)

Campus, IL · On-site

$239.47/hr

MACHINE LEARNING Course Code: MIA5100 Section: A Course Description: Posting limited to: Professeur a temps-partiel regulier / Regular Part-Time Professor Date Posted (YYYY/MM/DD): 2026/06/05 ...

Uottawa

APTPUO Fall 2026- MIA5100- (ONLINE)

Campus, IL · On-site

$239.47/hr

MACHINE LEARNING Course Code: MIA5100 Section: A Course Description: Posting limited to: Professeur a temps-partiel regulier / Regular Part-Time Professor Date Posted (YYYY/MM/DD): 2026/06/05 ...

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Part Time Nvidia Machine Learning information

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$25.5K

$42.6K

$88K

How much do part time nvidia machine learning jobs pay per year?

As of Jul 11, 2026, the average yearly pay for part time nvidia machine learning in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are some common challenges faced by part-time Nvidia Machine Learning professionals, and how can they be addressed?

Part-time Nvidia Machine Learning professionals often face challenges related to balancing project deadlines with limited working hours, as well as staying updated with rapidly evolving tools and frameworks. Collaborating efficiently with full-time team members and maintaining continuity in long-term projects can also be difficult. To address these challenges, clear communication, regular progress updates, and leveraging collaboration platforms are essential. Additionally, setting realistic goals and proactively seeking support from colleagues can help ensure smooth integration and continued professional development.

What are the key skills and qualifications needed to thrive as a Part Time Nvidia Machine Learning Engineer, and why are they important?

To thrive as a Part Time Nvidia Machine Learning Engineer, you need a solid background in machine learning, statistics, and programming (Python or C++), often supported by a relevant degree or coursework. Experience with Nvidia's CUDA, TensorRT, and deep learning frameworks like TensorFlow or PyTorch is typically required. Strong problem-solving skills, adaptability, and effective communication help you collaborate remotely and deliver impactful solutions. Mastery of these skills ensures efficient model development and deployment, leveraging Nvidia technologies to drive innovation in AI projects.

Can you work from home at NVIDIA?

Part Time NVIDIA Machine Learning roles may offer remote work options depending on the specific position and team. Many companies in the tech industry, including NVIDIA, provide flexible or remote work arrangements for roles involving machine learning and software development, especially if the job involves using tools like GPUs and programming in Python or CUDA. Candidates should review the job listing for remote work policies and requirements.

What are part-time Nvidia machine learning jobs?

Part-time Nvidia machine learning jobs are positions that involve working with Nvidia’s machine learning technologies, such as CUDA, TensorRT, and GPU-accelerated frameworks, but on a reduced or flexible schedule. These roles often include tasks like developing, optimizing, or testing machine learning models using Nvidia hardware and software. They are suitable for students, professionals seeking flexible hours, or those looking to gain experience in AI and deep learning without committing to a full-time role. Responsibilities can range from research and development to support and implementation of AI solutions.

What are the entry-level jobs at NVIDIA?

Entry-level jobs at NVIDIA for machine learning include roles such as Software Engineer Intern, Data Analyst, and Research Intern, which typically require foundational knowledge of programming, machine learning frameworks, and relevant coursework. These positions often offer opportunities to develop skills in AI, deep learning, and GPU computing, and may require a bachelor's degree or ongoing education in related fields.

What is the difference between Part Time Nvidia Machine Learning vs Part Time Data Scientist?

AspectPart Time Nvidia Machine LearningPart Time Data Scientist
Required CredentialsKnowledge of Nvidia tools, programming, basic ML conceptsStatistics, programming, data analysis skills
Work EnvironmentTech companies, research labs, AI startupsBusiness, finance, healthcare, tech firms
Industry UsageAI development, deep learning projectsData analysis, predictive modeling

While both roles involve data and programming, Part Time Nvidia Machine Learning focuses on AI and deep learning using Nvidia tools, whereas Part Time Data Scientist emphasizes data analysis and insights across various industries.

How difficult is it to get hired at NVIDIA?

Getting hired for a part-time Nvidia machine learning role typically requires a strong background in machine learning, programming skills in Python and frameworks like TensorFlow or PyTorch, and relevant experience or education. The hiring process is competitive and often involves technical interviews and assessments to evaluate technical proficiency and problem-solving abilities.
More about Part Time Nvidia Machine Learning jobs
What cities are hiring for Part Time Nvidia Machine Learning jobs? Cities with the most Part Time Nvidia Machine Learning job openings:
What are the most commonly searched types of Nvidia Machine Learning jobs? The most popular types of Nvidia Machine Learning jobs are:
What states have the most Part Time Nvidia Machine Learning jobs? States with the most job openings for Part Time Nvidia Machine Learning jobs include:
What job categories do people searching Part Time Nvidia Machine Learning jobs look for? The top searched job categories for Part Time Nvidia Machine Learning jobs are:
Part Time Nvidia Machine Learning jobs near you
Infographic showing various Part Time Nvidia Machine Learning job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 74% Full Time, 23% Part Time, 1% Temporary, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.

Adjunct Instructor- Artificial Intelligence- CTU Online

perdoceo

Remote

Part-time

Posted 2 days ago

Job description

Position Description
Consistent with and supportive of CTU's mission (to provide industry-relevant higher education to a diverse student population through innovative technology and experienced faculty, enabling the pursuit of personal and professional goals), Adjunct Faculty members provide quality and innovative instruction and meaningful engagement with our students to successfully achieve the relevant course, program, and degree level outcomes and support their academic success.
Responsibilities
  • Prepare relevant, insightful, and engaging instructional materials and utilize existing course materials that support learning by CTU's student population.
  • Provide instruction in assigned courses (including applicable laboratory or work that is integral to the courses) that aligns with CTU's curricula and outcomes, instructional modalities, course technologies, and faculty expectations.
  • Engage and communicate with students to encourage their course participation and learning while maintaining mutual respect and professionalism.
  • Relate professional/industry experience to CTU's Professional Learning Model by the continuation of professional/technical skills development, introduction of professional/industry perspectives into courses, and active awareness of professional/industry trends and opportunities.
  • Maintain accessibility for and provide timely responsiveness to students, academic/faculty leadership, and University staff by telephone, CTU e-mail, and other appropriate means of communication.
  • Establish and maintain weekly office hours for student questions/support.
  • Assess student performance on course assignments and provide assignment feedback to support continued student growth and development.
  • Maintain appropriate documentation of student course activities.
  • Work with appropriate CTU teams (e.g., advising, academic/faculty leadership, and University staff) and leverage appropriate information to identify and support students who may be exceptional or challenged in their coursework and/or educational endeavors.
  • Refer students to appropriate co-curricular and extra-curricular resources (e.g., advising, tutoring, library, learning centers, and career services).
  • Participate in and contribute to CTU's academic governance through attendance at appropriate University/college/program meetings and participation in the academic assessment and institutional effectiveness process (including completion of appropriate surveys and participation in continuous improvement initiatives).
  • Successfully complete required new faculty certification training, course-specific technology/pedagogical training, annual ethics and information technology policy training, and annual faculty development requirements.
  • Provide periodic required documentation of ongoing and updated licensures, certifications, immunizations (as appropriate to the specific college/program), scholarship, and academic/professional experience (e.g., CVs/resumes).
  • Work closely with Program Chair and/or Lead Faculty (as appropriate).
  • Perform other responsibilities and abide by the appropriate policies and procedures contained in CTU's Faculty Handbook.

Required Skills
  • Strong organizational and time management skills, with proficiency in meeting deadlines and urgency in responding to questions/requests.
  • Strong interpersonal and oral presentation/written communication skills.
  • Proficiency in working effectively, cooperatively, and flexibly in a team environment.
  • Proficiency with standard office and mobile applications (i.e., word processing, presentations, e-mail, calendaring, teleconferencing, text messaging, personal computers, and smart phones/tablets).

Required Experience
  • A Master's or PhD degree in Computer Science, or Electrical Engineering, or Computer Engineering.
  • Deep Knowledge of GPU Architecture: In-depth understanding of GPU architecture, including parallel computing, memory management, and performance optimization.
  • Experience with NVIDIA Technologies: Familiarity with NVIDIA technologies such as CUDA, TensorRT, and other tools and frameworks provided by NVIDIA.
  • Practical experience in the industry, particularly in roles involving GPU programming, AI, and machine learning applications.
  • Previous teaching experience at the university level, especially in courses related to computer architecture, parallel computing, or AI.
  • Strong verbal and written communication skills to effectively convey complex concepts to students.
  • Curriculum Development: Ability to develop and update course materials, including lectures, assignments, and exams, to keep pace with advancements in GPU technology and AI.
  • Research Contributions: Active involvement in research, particularly in areas related to GPU architecture and AI, can be a plus.
  • NVIDIA Certification: Certification from NVIDIA, such as becoming an NVIDIA-Certified Instructor, can be highly beneficial. This certification demonstrates proficiency in NVIDIA technologies and the ability to teach them effectively.

What we offer*
CTU generally compensates its Adjunct Faculty on a per quarter credit hour rate that takes into consideration a variety of factors, including campus (online and campus), degree level (undergraduate and graduate), and faculty rank (Instructor, Assistant Professor, Associate Professor, and Professor).
Job Type: Part-time
Pay: $331.50 - $603.64 credit hour
Benefits:
Flexible schedule
Tuition reimbursement (possible)
*Most benefits apply to full-time employees. Some benefits apply to part-time employees as well. Benefits may vary by location and position and are subject to change at any time. Ask your recruiter for full details and information about eligible dependents.
About Colorado Technical University (CTU)
For 55 years, Colorado Technical University has helped students fit a real-world education into their busy daily lives. With nearly 80 degree programs and concentrations in which students can pursue a variety of degrees at the Associate, Bachelor's, Master's and Doctoral level. CTU provides flexible online classes, accessible through the Virtual Campus and the innovative, award winning CTU Mobile app. CTU has two brick and mortar campus locations in Colorado Springs and Denver South, Colorado. With the help of faculty and industry professionals, CTU has awarded over 109,000 degrees to traditional campus and online students since 1965. For more information about CTU, visit www.coloradotech.edu.
Equal Opportunity Employer
Colorado Technical University is committed to a policy of equal employment opportunity and considers all persons without regard to age, color, disability, genetic information, marital status, national origin, race, religion, sex, sexual orientation, veteran status or any other status protected by applicable federal, state or local law.

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