STEM OPT Cloud Data Engineer Jobs
Cloud Data Engineer roles in cloud infrastructure, pipeline development, and data architecture qualify for STEM OPT's 24-month extension when your degree falls under an eligible CIP code. Your employer must be enrolled in E-Verify, and you'll need a signed I-983 training plan before your extension starts.
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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 STEM OPT Cloud Data Engineer Jobs
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Get Access To All JobsTips for Finding STEM OPT Authorization as a Cloud Data Engineer
Verify your CIP code before applying
Check that your STEM degree's CIP code appears on the DHS STEM Designated Degree Program List. Computer science, information systems, and data science codes commonly support Cloud Data Engineer roles, but exact coverage depends on your program's classification.
Confirm E-Verify enrollment before accepting offers
An employer must be actively enrolled in E-Verify before your STEM OPT extension is approved. Ask recruiters directly, or search the E-Verify employer search tool before your offer stage to avoid late-process surprises.
Map your training plan to cloud engineering tasks
Your I-983 must describe specific learning objectives tied to your actual role. For Cloud Data Engineer positions, list concrete skills like pipeline orchestration, cloud warehouse administration, or ETL framework development rather than generic software engineering goals.
Target employers with active LCA filings for data roles
Use Migrate Mate to filter Cloud Data Engineer postings by employers who have filed Labor Condition Applications for data engineering roles, giving you a verifiable signal of prior sponsorship activity before you invest time in any application.
Request the prevailing wage tier before negotiating
Look up the wage level for your target SOC code and metro area using the OFLC Wage Search before salary conversations. Employers filing an LCA must meet the prevailing wage, so knowing Level I through Level IV thresholds strengthens your negotiating position.
Apply 90 days before your OPT end date
USCIS requires your STEM OPT extension application to be filed while your initial OPT EAD is still valid. Submit your I-765 at least 90 days before expiration so any processing delay doesn't create an authorization gap between your initial and extended EAD.
Frequently Asked Questions
Does a Cloud Data Engineer role qualify for STEM OPT?
Cloud Data Engineer positions typically qualify when your employer classifies the role under a STEM-eligible SOC code and your degree falls under an approved CIP code on the DHS STEM Designated Degree Program List. Degrees in computer science, information systems, data science, and related engineering fields most commonly meet the requirement. Confirm the SOC code with your employer before filing.
What does the I-983 training plan need to include for this role?
Your I-983 must outline specific learning goals directly connected to your Cloud Data Engineer duties. Describe concrete technical skills you'll develop, such as designing distributed data pipelines, managing cloud storage systems, or building data models in a cloud warehouse. Vague objectives like 'gain industry experience' are insufficient. Both you and your employer sign the form, and your DSO must approve it before USCIS processes your extension.
How do I confirm my employer is enrolled in E-Verify?
You can search the E-Verify employer search tool by company name to confirm active enrollment. E-Verify participation is a hard requirement for STEM OPT approval. If your employer isn't enrolled, they must complete enrollment before your DSO can recommend the extension. Starting this conversation early in the offer process prevents delays after you've accepted a position.
What happens to my STEM OPT if my H-1B is selected in the lottery?
If your H-1B visa is selected and your employer files the petition before your STEM OPT expires, cap-gap protection extends your work authorization through September 30 of that fiscal year. You can continue working as a Cloud Data Engineer during this period without interruption. USCIS confirms cap-gap coverage through the petition receipt. Your EAD does not need to be reissued for cap-gap protection to apply.
Where can I find Cloud Data Engineer jobs that accept STEM OPT students?
Migrate Mate lists Cloud Data Engineer roles specifically filtered for employers enrolled in E-Verify, which is the foundational requirement for STEM OPT eligibility. You can search by role, location, and sponsorship history to identify employers who have previously supported OPT students through the extension process, reducing the risk of discovering mid-offer that an employer can't support your authorization.