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Nvidia Machine Learning Internship Jobs in Milwaukee, WI

Techvilla Solutions

ML Ops Engineer

Waukesha, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Milwaukee, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Germantown, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Mequon, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Fox Point, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

New Berlin, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Hartland, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Muskego, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Greendale, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Nashotah, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Brown Deer, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

North Lake, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Hubertus, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

Techvilla Solutions

ML Ops Engineer

Greenfield, WI · On-site

Deploy and manage machine learning models across cloud and on-premise environments. * Implement ... Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton. * Familiarity ...

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All JobsNvidia Machine Learning JobsNvidia Machine Learning Internship JobsNvidia Machine Learning Internship Jobs in Milwaukee, WI

Nvidia Machine Learning Internship information

See Milwaukee, WI salary details

$25.1K

$42K

$86.7K

How much do nvidia machine learning internship jobs pay per year?

As of Sep 6, 2026, the average yearly pay for nvidia machine learning internship in Milwaukee, WI is $41,955.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,000.00 and $45,300.00 per year, depending on experience, location, and employer.

What is an Nvidia machine learning internship?

An Nvidia Machine Learning Internship is a temporary, hands-on program for students or recent graduates to work with Nvidia’s teams on projects related to machine learning and artificial intelligence. Interns typically assist with research, data analysis, model development, and software engineering tasks using Nvidia’s cutting-edge GPU technologies. The internship provides valuable real-world experience, mentorship from industry experts, and the opportunity to contribute to innovative AI solutions. It’s a great way to build skills, expand your professional network, and potentially secure a full-time role at Nvidia in the future.

What types of projects do interns typically work on during the Nvidia machine learning internship?

During the Nvidia Machine Learning Internship, interns often work on real-world projects involving deep learning, computer vision, or natural language processing. These projects may include developing new models, optimizing existing algorithms, or contributing to open-source frameworks. Interns typically collaborate with experienced engineers and researchers, gaining hands-on experience while having access to state-of-the-art GPU hardware. The work environment encourages innovation and learning, and interns are often given opportunities to present their results to senior team members.

What are the key skills and qualifications needed to thrive as an Nvidia machine learning intern, and why are they important?

To excel as an Nvidia Machine Learning Intern, you need a solid foundation in computer science, mathematics, and machine learning concepts, typically supported by progress toward a relevant degree. Familiarity with programming languages like Python, deep learning frameworks such as TensorFlow or PyTorch, and GPU computing tools (e.g., CUDA) is essential. Strong analytical thinking, problem-solving skills, and effective teamwork set standout interns apart. These competencies enable you to contribute meaningfully to advanced AI projects and collaborate efficiently within Nvidia's innovative environment.

What is the difference between Nvidia Machine Learning Internship vs Data Science Internship?

AspectNvidia Machine Learning InternshipData Science Internship
Required CredentialsRelevant coursework, programming skills, possibly some machine learning certificationsStatistics, programming, data analysis skills, often a related degree
Work EnvironmentResearch labs, tech company offices, collaborative teams focused on AI/ML projectsBusiness environments, data analysis teams, cross-functional collaboration
Employer & Industry UsageTech companies, AI/ML research labs, hardware/software firms like NvidiaVarious industries including tech, finance, healthcare, and consulting

While both internships involve working with data and programming, Nvidia Machine Learning Internships focus specifically on developing and optimizing machine learning models in a hardware and AI context, whereas Data Science Internships emphasize analyzing data to derive insights across diverse industries.

What are popular job titles related to Nvidia Machine Learning Internship jobs in Milwaukee, WI?

For Nvidia Machine Learning Internship jobs in Milwaukee, WI, the most frequently searched job titles are:

What job categories do people searching Nvidia Machine Learning Internship jobs in Milwaukee, WI look for?

The top searched job categories for Nvidia Machine Learning Internship jobs in Milwaukee, WI are:

Nvidia Machine Learning Internship jobs near you

ML Ops Engineer

Techvilla Solutions

Waukesha, WI • On-site

Full-time

Posted 16 days ago

Job description

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine learning infrastructure and deployment pipelines. The ideal candidate will have strong experience with cloud platforms, CI/CD, containerization, model deployment, monitoring, and ML lifecycle management.

Roles and Responsibilities
  • Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining.
  • Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration.
  • Deploy and manage machine learning models across cloud and on-premise environments.
  • Implement model versioning, experiment tracking, feature management, and model governance.
  • Build scalable infrastructure using Docker, Kubernetes, and cloud services.
  • Monitor model performance, data quality, system health, and production workloads.
  • Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams.
  • Troubleshoot production ML systems and optimize reliability, scalability, and performance.
  • Implement security, access controls, logging, and compliance best practices.
Required Skills
  • 5+ years of experience in DevOps, ML Engineering, MLOps, or a related field.
  • Strong experience with MLOps concepts and ML lifecycle management.
  • Hands-on experience with Python and scripting.
  • Experience with AWS, Azure, or GCP.
  • Strong knowledge of Docker and Kubernetes.
  • Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
  • Experience with MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar ML platforms.
  • Experience with Git, Terraform, and infrastructure automation.
  • Knowledge of model monitoring, observability, data validation, and model performance tracking.
  • Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures.
Preferred Skills
  • Experience with Apache Airflow, Databricks, Spark, or Kafka.
  • Knowledge of LLMOps/GenAI deployment and monitoring.
  • Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton.
  • Familiarity with Prometheus, Grafana, ELK, or similar observability tools.
  • Understanding of ML security, governance, and responsible AI practices.
Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.

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