STEM OPT Cloud Software Engineer Jobs
Cloud Software Engineer roles in cloud infrastructure, platform engineering, and distributed systems qualify for the 24-month STEM OPT extension if your degree falls under an eligible CIP code. Your employer must be enrolled in E-Verify before you start, and you'll need an approved I-983 training plan on file with your DSO.
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Get Access To All JobsINTRODUCTION
We are seeking an ML/AI Data & Cloud Engineer to design, build, and operate the cloud-native components that deliver large-scale, data-intensive AI capabilities.
This role focuses on building scalable data platforms, distributed processing systems, and model-serving infrastructure that enable end-to-end machine learning workflows in production. You will work closely with data scientists and product teams to transform advanced models into reliable, high-performance services.
The ideal candidate combines strong engineering skills with a solid grounding in quantitative methods, and is comfortable leveraging modern AI-assisted development tools to accelerate research, prototyping, and delivery.
Responsibilities
- Design and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems
- Build and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows
- Architect data processing systems for feature engineering, large-scale computation, and model training support
- Develop and operate model-serving infrastructure enabling real-time and batch inference via APIs
- Develop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)
- Implement model validation and monitoring frameworks addressing bias, drift, robustness, and performance
- Tune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers
- Build and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining
- Ensure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)
- 3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development
- Strong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling
- Experience designing and operating cloud-native systems (AWS or equivalent)
- Experience building and maintaining large-scale data pipelines and working with high-volume datasets
- Experience developing APIs or microservices for production systems
- Solid understanding of distributed systems, data processing, and system reliability concepts
- Solid foundation in algorithms, data structures, and computational thinking
PREFERRED QUALIFICATIONS
- Familiarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)
- Exposure to machine learning workflows and the infrastructure required to support training and inference at scale
- Experience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, SageMaker, or equivalents)
- Experience with containerization (Docker/Podman, ECS)
- Experience with data lake architectures and multi-source data ingestion patterns
- Familiarity with MLOps practices, including reproducibility, model versioning, and deployment automation
- Working knowledge of statistics, econometrics, quantitative modeling, and optimization
- Familiarity with cloud cost optimization strategies and performance tuning
WHAT YOU’LL WORK ON
- Scalable cloud data platforms integrating diverse, high-volume datasets
- Foundational predictive models that adapt across markets rather than being rebuilt for each use case
- A cloud-native platform supporting data ingestion, model training, inference, and scalable deployment
- Intelligent systems for ranking, matching, scoring, and explanation of model outputs
- Robust evaluation and monitoring frameworks for model performance and reliability
- API-driven integration of AI capabilities into broader software ecosystems
WHY JOIN US?
You’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. This role offers the opportunity to build foundational platforms that support advanced AI capabilities while shaping architecture, tooling, and best practices in a rapidly evolving space.
INTRODUCTION
We are seeking an ML/AI Data & Cloud Engineer to design, build, and operate the cloud-native components that deliver large-scale, data-intensive AI capabilities.
This role focuses on building scalable data platforms, distributed processing systems, and model-serving infrastructure that enable end-to-end machine learning workflows in production. You will work closely with data scientists and product teams to transform advanced models into reliable, high-performance services.
The ideal candidate combines strong engineering skills with a solid grounding in quantitative methods, and is comfortable leveraging modern AI-assisted development tools to accelerate research, prototyping, and delivery.
Responsibilities
- Design and implement cloud-native data platforms that integrate structured and unstructured data into scalable data lakes or unified data systems
- Build and maintain robust ingestion pipelines using API-driven, streaming, and batch processing workflows
- Architect data processing systems for feature engineering, large-scale computation, and model training support
- Develop and operate model-serving infrastructure enabling real-time and batch inference via APIs
- Develop advanced modeling approaches combining statistical methods and machine learning (e.g., regression, gradient boosting, random forests, and ensemble/meta-learning techniques)
- Implement model validation and monitoring frameworks addressing bias, drift, robustness, and performance
- Tune cloud infrastructure for performance, reliability, and cost efficiency across compute, storage, and data transfer layers
- Build and maintain MLOps pipelines for model deployment, versioning, monitoring, and automated retraining
- Ensure platform reliability, fault tolerance, and data integrity across all stages of the data and model lifecycle
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Engineering, Engineering, Applied Mathematics, or related field (or equivalent experience)
- 3+ years of experience in data engineering, cloud engineering, ML platform engineering, machine learning engineering, AI systems, quantitative modeling, or data-intensive platform development
- Strong programming skills in Python and experience with backend, data engineering, or experience with modern ML frameworks and data tooling
- Experience designing and operating cloud-native systems (AWS or equivalent)
- Experience building and maintaining large-scale data pipelines and working with high-volume datasets
- Experience developing APIs or microservices for production systems
- Solid understanding of distributed systems, data processing, and system reliability concepts
- Solid foundation in algorithms, data structures, and computational thinking
PREFERRED QUALIFICATIONS
- Familiarity with modern AI-assisted development tools and workflows (e.g., LLM-based coding assistants, agentic development environments, or similar tools)
- Exposure to machine learning workflows and the infrastructure required to support training and inference at scale
- Experience with cloud services (e.g., AWS: S3, EC2, Lambda, Glue, SageMaker, or equivalents)
- Experience with containerization (Docker/Podman, ECS)
- Experience with data lake architectures and multi-source data ingestion patterns
- Familiarity with MLOps practices, including reproducibility, model versioning, and deployment automation
- Working knowledge of statistics, econometrics, quantitative modeling, and optimization
- Familiarity with cloud cost optimization strategies and performance tuning
WHAT YOU’LL WORK ON
- Scalable cloud data platforms integrating diverse, high-volume datasets
- Foundational predictive models that adapt across markets rather than being rebuilt for each use case
- A cloud-native platform supporting data ingestion, model training, inference, and scalable deployment
- Intelligent systems for ranking, matching, scoring, and explanation of model outputs
- Robust evaluation and monitoring frameworks for model performance and reliability
- API-driven integration of AI capabilities into broader software ecosystems
WHY JOIN US?
You’ll work on challenging, high-scale systems at the intersection of data engineering, cloud infrastructure, and machine learning. This role offers the opportunity to build foundational platforms that support advanced AI capabilities while shaping architecture, tooling, and best practices in a rapidly evolving space.
See all 344+ STEM OPT Cloud Software Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new STEM OPT Cloud Software Engineer Jobs.
Get Access To All JobsTips for Finding STEM OPT Authorization as a Cloud Software Engineer
Verify your CIP code before applying
Pull your official transcript and confirm your degree's CIP code maps to an eligible STEM field. Computer Science (11.0701) and Computer Engineering (14.0901) both qualify, but Interdisciplinary or IT Management degrees sometimes don't. Check the DHS STEM Designated Degree Program List before your OPT end date.
Filter for E-Verify employers before outreach
Confirm E-Verify enrollment before you submit an application, not after you receive an offer. Search the E-Verify employer search tool by company name to check enrollment status. Startups and small studios frequently recruit STEM talent but skip E-Verify enrollment, which disqualifies them for your extension.
Draft your I-983 training plan before offer negotiation
Cloud engineering roles require a detailed I-983 that maps your training activities to specific learning objectives. Prepare a working draft showing how the role develops your STEM competencies so your employer can review and sign it without delays that could push your start date past your OPT deadline.
Target cloud teams at E-Verify-enrolled contractors
Federal contractors are legally required to enroll in E-Verify, making them reliable targets for STEM OPT students. Cloud engineering demand is high across defense tech, government cloud integrators, and health IT contractors who regularly hire for AWS, Azure, and GCP infrastructure roles.
Use Migrate Mate to find verified cloud roles
Search Migrate Mate for Cloud Software Engineer positions filtered by employers with active STEM OPT hiring history. You can identify which companies have filed for STEM-eligible roles and cross-reference E-Verify enrollment before committing time to the application process.
Understand cap-gap protection if your H-1B is selected
If you're selected in the H-1B lottery while on STEM OPT, cap-gap automatically extends your work authorization through September 30. File your H-1B visa registration during your second or third OPT year so your cloud engineering role continues uninterrupted during the transition period.
Frequently Asked Questions
Does a Cloud Software Engineer role qualify for the STEM OPT extension?
Yes, provided your job duties align with your STEM degree field and your employer is enrolled in E-Verify. Cloud Software Engineer positions typically fall under SOC codes for Software Developers or Computer Network Architects. Confirm the role's SOC code with your employer before filing, and verify your degree's CIP code appears on the DHS STEM Designated Degree Program List.
What STEM degrees make you eligible for the extension in this field?
Degrees in Computer Science, Computer Engineering, Electrical Engineering, Information Systems, and Mathematics commonly qualify. Your degree's CIP code must appear on the DHS STEM Designated Degree Program List. Business-adjacent degrees like Management Information Systems may qualify under certain CIP codes but require verification with your DSO before you apply for the extension through USCIS.
How does the I-983 training plan work for a cloud engineering position?
Your employer and DSO both sign the I-983, which documents how the Cloud Software Engineer role provides practical training in your STEM field. You'll list specific learning objectives tied to cloud architecture, systems design, or your relevant discipline. USCIS requires that the training is commensurate with your degree level, so vague job descriptions won't satisfy the form's requirements.
What happens if my employer isn't enrolled in E-Verify?
You cannot work for that employer on STEM OPT. E-Verify enrollment is a mandatory requirement, and the employer must be enrolled before your extension start date. If you've already received an offer, ask the recruiter or HR team to enroll immediately. Enrollment typically takes a few business days, but your DSO needs the confirmation before authorizing your extension.
Where can I find Cloud Software Engineer jobs that support STEM OPT?
Migrate Mate lists Cloud Software Engineer roles from employers with verified STEM OPT hiring history, so you can identify E-Verify-enrolled companies without contacting each one individually. Filtering by role and authorization type narrows your search to employers already familiar with the I-983 process and the reporting requirements that come with hiring STEM OPT students.