STEM OPT Mlops Engineer Jobs
MLOps Engineer roles sit squarely within STEM-designated fields, making them a strong fit for F-1 students on the 24-month STEM OPT extension. Your employer must be enrolled in E-Verify and sign a Form I-983 training plan. A STEM degree in computer science, data science, or a related CIP-coded field is required to qualify.
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Get Access To All JobsINTRODUCTION
If you're ready to be part of our legacy of hope and innovation, we encourage you to take the first step and explore our current job openings. Your best is waiting to be discovered.
ROLE AND RESPONSIBILITIES
We are seeking a high-caliber Senior AI Platform & ML Ops Engineer to architect the "layered" infrastructure required for autonomous, agentic systems within Stanford Healthcare. In this role, you will be the "Master Chef" of our AI ecosystem, seamlessly folding Expert-Level DevOps (Kubernetes, Terraform, DevOps orchestration) with Agentic Application Development (LangGraph, CrewAI, Tool-calling logic). You won't just manage servers; you will build the robust, full-stack "factory" where multi-agent frameworks interact with healthcare APIs, ensuring every autonomous action is governed by strict ML Ops observability (LangSmith, Arize) and safety guardrails. If you have the "crispy" coding skills to build RAG pipelines in Python and the "rich" architectural depth to deploy scalable microservices, extensive full stack software development expertise, we want you to lead the integration of reasoning-based AI into the future of clinical and business workflow automations.
The MLOPs Engineer will play an integral role incorporating Artificial Intelligence (AI) within Stanford Health Care. The solutions will impact patient care, medical research, and operational services. This group is tasked to innovate, build, deploy and monitor production grade AI, machine learning (ML) and predictive algorithms into healthcare. The role will partner closely with lead researchers within the AI field and leaders across various clinical specialties and operations.
This role will report to the Infrastructure group and have a dotted line relationship to the Data Science team. The role will be responsible for maintaining cloud-based infrastructure as code repositories, maintaining infrastructure, deployment pipelines and designing the security landscape for the team and objects. The role will set the standards for the full SDLC of projects for the Data Science team.
LOCATION
Stanford Health Care
What you will do
- Design, build and maintain scalable and robust infrastructure for AI/ML systems, including cloud-based environments, containerization and orchestration platforms.
- Develop and implement CI/CD pipelines to automate the deployment, testing and monitoring of AI/ML models and applications.
- Collaborate with data scientists, data engineers and software engineers to optimize model training, deployment and inference pipelines.
- Monitor and troubleshoot AI/ML systems to ensure high availability, performance and reliability.
- Maintain and monitor model training and inference pipelines across multi-cloud tenants especially around Large Language Models (LLMs).
- Maintain Kubernetes pods, container registry and virtual machine image library and model registry.
- Monitor infrastructure utilization and costs pertaining to model training, inference and GPU utilization.
- Implement best practices for security, data privacy and compliance in AI/ML workflows and infrastructure.
- Evaluate and integrate new tools, technologies and frameworks to improve the efficiency and effectiveness of our MLOps processes.
- Mentor and provide technical guidance to junior members of the organization.
- Stay up-to-date with the latest advancements and trends in MLOps, DevOps and cloud technologies and share them with the team.
EDUCATION QUALIFICATIONS
Bachelor’s or higher degree in Computer Science, Engineering or a related field
EXPERIENCE QUALIFICATIONS
Three (3) or more years of directly related experience
REQUIRED KNOWLEDGE, SKILLS AND ABILITIES
- Proven experience as an MLOps Engineer.
- Strong knowledge of cloud platforms such as AWS, Azure or Google Cloud and experience with infrastructure-as-code tools like Terraform or CloudFormation.
- Proficiency in containerization technologies such as Docker and container orchestration platforms like Kubernetes.
- Experience with CI/CD tools such as GitLab CI/CD, Github Actions or CircleCI.
- Solid programming skills in languages such as Python, Rust or Go and experience in scripting and automation.
- Familiarity with machine learning frameworks and libraries such as PyTorch, Tensorflow and scikit-learn.
- Deep understanding of DevOps principles, agile methodologies and software development lifecycle.
- Strong problem-solving and troubleshooting skills, with the ability to analyze and resolve complex technical issues.
- Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams.
PHYSICAL DEMANDS AND WORK CONDITIONS
Blood Borne Pathogens
Category III - Tasks that involve NO exposure to blood, body fluids or tissues, and Category I tasks that are not a condition of employment
These principles apply to ALL employees:
Stanford Health Care sets a high standard for delivering value and an exceptional experience for our patients and families. Candidates for employment and existing employees must adopt and execute C-I-CARE standards for all of patients, families and towards each other. C-I-CARE is the foundation of Stanford’s patient-experience and represents a framework for patient-centered interactions. Simply put, we do what it takes to enable and empower patients and families to focus on health, healing and recovery.
You will do this by executing against our three experience pillars, from the patient and family’s perspective:
- Know Me: Anticipate my needs and status to deliver effective care
- Show Me the Way: Guide and prompt my actions to arrive at better outcomes and better health
- Coordinate for Me: Own the complexity of my care through coordination
Equal Opportunity Employer
Stanford Health Care (SHC) strongly values diversity and is committed to equal opportunity and non-discrimination in all of its policies and practices, including the area of employment. Accordingly, SHC does not discriminate against any person on the basis of race, color, sex, sexual orientation or gender identity and/or expression, religion, age, national or ethnic origin, political beliefs, marital status, medical condition, genetic information, veteran status, or disability, or the perception of any of the above. People of all genders, members of all racial and ethnic groups, people with disabilities, and veterans are encouraged to apply. Qualified applicants with criminal convictions will be considered after an individualized assessment of the conviction and the job requirements.
COMPENSATION
Base Pay Scale: Generally starting at $79.21 - $104.97 per hour
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training. This pay scale is not a promise of a particular wage.
INTRODUCTION
If you're ready to be part of our legacy of hope and innovation, we encourage you to take the first step and explore our current job openings. Your best is waiting to be discovered.
ROLE AND RESPONSIBILITIES
We are seeking a high-caliber Senior AI Platform & ML Ops Engineer to architect the "layered" infrastructure required for autonomous, agentic systems within Stanford Healthcare. In this role, you will be the "Master Chef" of our AI ecosystem, seamlessly folding Expert-Level DevOps (Kubernetes, Terraform, DevOps orchestration) with Agentic Application Development (LangGraph, CrewAI, Tool-calling logic). You won't just manage servers; you will build the robust, full-stack "factory" where multi-agent frameworks interact with healthcare APIs, ensuring every autonomous action is governed by strict ML Ops observability (LangSmith, Arize) and safety guardrails. If you have the "crispy" coding skills to build RAG pipelines in Python and the "rich" architectural depth to deploy scalable microservices, extensive full stack software development expertise, we want you to lead the integration of reasoning-based AI into the future of clinical and business workflow automations.
The MLOPs Engineer will play an integral role incorporating Artificial Intelligence (AI) within Stanford Health Care. The solutions will impact patient care, medical research, and operational services. This group is tasked to innovate, build, deploy and monitor production grade AI, machine learning (ML) and predictive algorithms into healthcare. The role will partner closely with lead researchers within the AI field and leaders across various clinical specialties and operations.
This role will report to the Infrastructure group and have a dotted line relationship to the Data Science team. The role will be responsible for maintaining cloud-based infrastructure as code repositories, maintaining infrastructure, deployment pipelines and designing the security landscape for the team and objects. The role will set the standards for the full SDLC of projects for the Data Science team.
LOCATION
Stanford Health Care
What you will do
- Design, build and maintain scalable and robust infrastructure for AI/ML systems, including cloud-based environments, containerization and orchestration platforms.
- Develop and implement CI/CD pipelines to automate the deployment, testing and monitoring of AI/ML models and applications.
- Collaborate with data scientists, data engineers and software engineers to optimize model training, deployment and inference pipelines.
- Monitor and troubleshoot AI/ML systems to ensure high availability, performance and reliability.
- Maintain and monitor model training and inference pipelines across multi-cloud tenants especially around Large Language Models (LLMs).
- Maintain Kubernetes pods, container registry and virtual machine image library and model registry.
- Monitor infrastructure utilization and costs pertaining to model training, inference and GPU utilization.
- Implement best practices for security, data privacy and compliance in AI/ML workflows and infrastructure.
- Evaluate and integrate new tools, technologies and frameworks to improve the efficiency and effectiveness of our MLOps processes.
- Mentor and provide technical guidance to junior members of the organization.
- Stay up-to-date with the latest advancements and trends in MLOps, DevOps and cloud technologies and share them with the team.
EDUCATION QUALIFICATIONS
Bachelor’s or higher degree in Computer Science, Engineering or a related field
EXPERIENCE QUALIFICATIONS
Three (3) or more years of directly related experience
REQUIRED KNOWLEDGE, SKILLS AND ABILITIES
- Proven experience as an MLOps Engineer.
- Strong knowledge of cloud platforms such as AWS, Azure or Google Cloud and experience with infrastructure-as-code tools like Terraform or CloudFormation.
- Proficiency in containerization technologies such as Docker and container orchestration platforms like Kubernetes.
- Experience with CI/CD tools such as GitLab CI/CD, Github Actions or CircleCI.
- Solid programming skills in languages such as Python, Rust or Go and experience in scripting and automation.
- Familiarity with machine learning frameworks and libraries such as PyTorch, Tensorflow and scikit-learn.
- Deep understanding of DevOps principles, agile methodologies and software development lifecycle.
- Strong problem-solving and troubleshooting skills, with the ability to analyze and resolve complex technical issues.
- Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams.
PHYSICAL DEMANDS AND WORK CONDITIONS
Blood Borne Pathogens
Category III - Tasks that involve NO exposure to blood, body fluids or tissues, and Category I tasks that are not a condition of employment
These principles apply to ALL employees:
Stanford Health Care sets a high standard for delivering value and an exceptional experience for our patients and families. Candidates for employment and existing employees must adopt and execute C-I-CARE standards for all of patients, families and towards each other. C-I-CARE is the foundation of Stanford’s patient-experience and represents a framework for patient-centered interactions. Simply put, we do what it takes to enable and empower patients and families to focus on health, healing and recovery.
You will do this by executing against our three experience pillars, from the patient and family’s perspective:
- Know Me: Anticipate my needs and status to deliver effective care
- Show Me the Way: Guide and prompt my actions to arrive at better outcomes and better health
- Coordinate for Me: Own the complexity of my care through coordination
Equal Opportunity Employer
Stanford Health Care (SHC) strongly values diversity and is committed to equal opportunity and non-discrimination in all of its policies and practices, including the area of employment. Accordingly, SHC does not discriminate against any person on the basis of race, color, sex, sexual orientation or gender identity and/or expression, religion, age, national or ethnic origin, political beliefs, marital status, medical condition, genetic information, veteran status, or disability, or the perception of any of the above. People of all genders, members of all racial and ethnic groups, people with disabilities, and veterans are encouraged to apply. Qualified applicants with criminal convictions will be considered after an individualized assessment of the conviction and the job requirements.
COMPENSATION
Base Pay Scale: Generally starting at $79.21 - $104.97 per hour
The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to, internal equity, experience, education, specialty and training. This pay scale is not a promise of a particular wage.
See all Mlops Engineer jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Mlops Engineer roles.
Get Access To All JobsTips for Finding STEM OPT Authorization as a Mlops Engineer
Verify your CIP code before applying
Check that your degree's Classification of Instructional Programs code maps to an approved STEM field. Computer science, information systems, and engineering CIP codes qualify, but some interdisciplinary programs don't. Confirm with your DSO before your OPT end date.
Confirm E-Verify enrollment during screening
Ask recruiters directly whether the company is enrolled in E-Verify before accepting any offer. An employer that isn't enrolled cannot legally employ you on STEM OPT, regardless of how the role is structured or what the contract says.
Frame your I-983 training plan around MLOps outcomes
Generic training plan language gets flagged. Tie your I-983 learning objectives to specific MLOps competencies: model deployment pipelines, CI/CD for ML, monitoring infrastructure. Your supervisor must co-sign, so align the plan with their actual team roadmap.
Target companies with active ML infrastructure teams
Use Migrate Mate to filter for employers with verified E-Verify enrollment and a history of hiring STEM OPT workers in software and data engineering roles, so you spend time on companies already set up to onboard you legally.
Benchmark your offer against DOL prevailing wages
Before signing, look up the prevailing wage for MLOps or machine learning engineer roles in your metro area using the OFLC Wage Search. Your employer's LCA must certify wages at or above that level, and a low offer can signal a compliance problem.
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Find Mlops Engineer JobsFrequently Asked Questions
Does an MLOps Engineer role qualify for the STEM OPT extension?
Yes, if your degree is in a STEM-designated field such as computer science, electrical engineering, or data science and carries an approved CIP code. The MLOps Engineer occupation falls under software and systems engineering, which USCIS recognizes as STEM-eligible. Confirm your specific CIP code with your DSO before filing your extension application.
What does my employer need to do to support my STEM OPT as an MLOps Engineer?
Your employer must be enrolled in E-Verify and must co-sign a Form I-983 training plan that outlines the practical training you'll receive. For an MLOps role, that plan should specify learning goals tied to ML pipeline development, model monitoring, or infrastructure automation. The employer also commits to reporting any material changes in your role or hours to your DSO within five business days.
How does the cap-gap rule protect me if I'm an MLOps Engineer waiting on H-1B selection?
If your employer files an H-1B petition before your STEM OPT expires and you're selected in the lottery, the cap-gap rule automatically extends your work authorization through September 30 of that fiscal year. You can continue working as an MLOps Engineer during that period without interruption, as long as the petition remains pending or approved.
What should an I-983 training plan include for an MLOps Engineer position?
The I-983 must list specific learning objectives relevant to your MLOps role, the supervision structure, how the work connects to your STEM degree, and a timeline for achieving those goals. Vague descriptions like 'software development' aren't sufficient. Detail tasks such as building deployment pipelines, automating model retraining workflows, or implementing observability tooling so the plan reflects what you'll actually do.
Where can I find MLOps Engineer jobs where the employer is already set up for STEM OPT?
Migrate Mate lists MLOps Engineer roles from employers verified as E-Verify enrolled, which is the baseline requirement for any STEM OPT position. Filtering by E-Verify status upfront saves you from pursuing roles at companies that can't legally hire you on an OPT extension, and lets you focus your applications where the compliance groundwork is already in place.
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