Intel
Cloud Analytics - Application PhD Intern
US, Oregon, Hillsboro · Intern · Internship
Sponsorship not specifiedDetected 1 day ago
PythonData StructuresAlgorithmsGitSQLDockerKubernetesCI/CDJenkinsDevOpsMachine LearningPyTorchComputer VisionMLOpsProblem Solving
Stay score
odds of building a lasting career here
42Risky
Cap-exempt (no lottery)0
Sponsors this role100
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
Lottery odds assume a STEM candidate.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
Green-card filing pattern in this occupation
Context, not a finding about this posting: of this employer's 333 green-card filings in this occupation, 100% were for a worker who already held the job.
Green-card follow-through: 74%
Of 318 labor certifications old enough to have been used, 83 expired without the employer filing the next step. Median time from filing to decision: 508 days.100% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.
Reported a layoff in this occupation
In a green-card filing decided 2026-05-14, this employer stated it had laid off workers in Software Developers (CA) during the preceding six months. 3 such filings on record.Describes the period before that filing, not necessarily today. Unlike WARN notices this is occupation-specific and federal.
Files H-1B transfers
33 transfer filings in the last year, covering 33 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- You'll play an integral role in creating scalable ML pipelines, optimizing cloud applications, and implementing MLOps practices.
- As part of Intel Foundry, you will work within a dynamic group focused on advancing Intel's manufacturing and software capabilities.
- This team operates at the forefront of innovation, leveraging machine learning and cloud technologies to enhance operational efficiency, performance, and scalability.
Responsibilities
- Develop and deploy APIs and microservices that interact with various components of ML-powered applications.
- Utilize containerization technologies such as Docker and orchestration systems like Kubernetes to package, scale, and manage ML services.
- Implement MLOps best practices, including model versioning, monitoring, and continuous integration/continuous deployment (CI/CD).
- Collaborate cross-functionally with software developers, data scientists, and DevOps engineers to integrate ML capabilities into Intel's manufacturing processes seamlessly.
- Optimize the performance, latency, and cost efficiency of deployed ML models.
- This opportunity allows you to collaborate with experienced engineers to bring ML innovations to life in production settings, directly supporting Intel's cutting-edge manufacturing processes.
- This role aligns with Intel's broader mission to drive technological transformation and create world-changing advancements.
Requirements
- Hands-on experience with Docker and cloud deployment technologies.
- Familiarity with CI/CD tools and practices such as Jenkins, GitLab CI, or GitHub Actions.
- Our standard internship rates are based on your degree, location, and the job role.
- Currently enrolled in Ph.D. program in Computer Science, Software Engineering, or a related technical field.
- Strong programming skills in Python, and solid understanding of software engineering principles, including data structures, algorithms, and object-oriented design.
- Prior experience working with python client libraries to interact with SQL based databases, optimize queries and perform inserts/updates per application needs.
- Knowledge of machine learning concepts and the model development lifecycle.
Nice to have
- Experience with MLOps tools like MLflow or Kubeflow.
- Familiarity with Kubernetes and Rancher for orchestration.
- Demonstrated ability to work collaboratively in a cross-functional team environment.
- 3.0 GPA or higher is preferred along with previous internship experience in an enterprise environment.
- Take the next step in your career and join Intel's innovative team, where your contributions will help shape the future of technology.
- Apply today to make an impact.
- Student / Intern
- Shift 1 (United States of America)
Skills
- Business group:
Compensation
- Range for jobs which could be performed in the US: $137,698.00-137,702.00 USD
- The range displayed on this job posting reflects the minimum and maximum target compensation for the position across all US locations.
- Your recruiter can share more about the specific compensation range for your preferred location and job role during the hiring process.
- Annual Salary Range for jobs which could be performed in the US:
- $137,698.00-137,702.00 USD
Benefits
- It consists of competitive pay, stock bonuses, and benefit programs which include health, retirement, and vacation.
- Find out more about the benefits of working at Intel.
- Candidates should never be required to pay recruitment fees, medical examination fees, or any other charges as a condition of employment.
- Design, develop, and maintain scalable machine learning pipelines for data processing, model training, and inference in cloud and on-premises environments.
Company info
- Business group: As part of Intel Foundry, you will work within a dynamic group focused on advancing Intel's manufacturing and software capabilities.
- We offer a total compensation package that ranks among the best in the industry.
This listing is sourced directly from Intel's careers page and normalized into a canonical job model.