Mlops Engineer Jobs for OPT Students
MLOps Engineer jobs on OPT sit at the intersection of machine learning and production infrastructure, making them highly attractive to F-1 students with STEM-designated degrees. Most roles qualify for the 24-month STEM OPT extension. Employers in this space regularly file H-1B petitions, giving you a clear path beyond your initial OPT period.
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Get Access To All JobsINTRODUCTION
At Weyerhaeuser, we sustainably manage forests and manufacture products that make the world a better place. With a commitment to excellence and innovation, we leverage technology to enhance operational efficiency across timberlands, wood products, and corporate functions. As we continue to scale AI across the enterprise, we are seeking a skilled MLOps Engineer to operationalize machine learning solutions and ensure they are reliable, scalable, secure, and delivering measurable business value in production.
ROLE AND RESPONSIBILITIES
The MLOps Engineer will be responsible for building, deploying, monitoring, and operating machine learning systems across Weyerhaeuser’s AI portfolio, including pricing optimization, industrial AI, geospatial analytics, and generative AI solutions. This role sits at the intersection of data science, software engineering, and cloud infrastructure, enabling the transition from experimental models to trusted, production-grade AI services. You will work closely with data scientists, AI engineers, product managers, and platform teams to implement standardized MLOps patterns that support repeatability, governance, and continuous improvement across the AI lifecycle. The ideal candidate has hands-on experience with ML deployment pipelines, cloud-native infrastructure, model monitoring, and enterprise data platforms—and is motivated by building systems that scale responsibly.
Primary Responsibilities
Operationalize Machine Learning Models
- Design, build, and maintain end-to-end MLOps pipelines that support model training, validation, deployment, and automatic retraining across multiple AI use cases.
Model Deployment & Serving
- Deploy batch and real-time inference workloads using cloud-native services and containerized architectures, ensuring performance, reliability, and cost efficiency.
Monitoring & Observability
- Implement robust monitoring for model performance, data drift, prediction quality, latency, and system health. Establish alerting and diagnostics to support rapid issue detection and remediation.
CI/CD for AI Systems
- Develop and maintain CI/CD workflows for machine learning assets, including code, features, models, and configurations, enabling safe and repeatable releases.
Data & Feature Pipelines
- Collaborate with data engineering teams to ensure reliable data ingestion, feature generation, and versioning to support consistent model behavior across environments. Design, build and support online and offline feature stores.
Governance & Responsible AI
- Support enterprise AI governance by enabling model lineage, reproducibility, auditability, and controlled promotion across environments in alignment with Responsible AI principles.
Cross-Functional Collaboration
- Partner with data scientists, AI engineers, product managers, IT, and cybersecurity teams to architect and implement modeling work into production-ready services.
Platform Enablement
- Contribute to shared MLOps tooling, standards, and reference architectures that accelerate AI delivery across Weyerhaeuser’s AI Factory.
Continuous Improvement
- Identify opportunities to improve reliability, automation, scalability, and developer experience across the AI delivery lifecycle.
BASIC QUALIFICATIONS
Education
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field; advanced degree is a plus.
Experience
- 4-6 years of experience building and supporting production machine learning systems, data platforms, or cloud-native software services in enterprise environments.
MLOps & ML Systems
- Hands-on experience with model lifecycle management, including training pipelines, model registries, deployment strategies, and monitoring.
Cloud & Infrastructure
- Experience with cloud platforms such as AWS or Azure, including containerization (Docker), orchestration (Kubernetes or managed equivalents), and infrastructure-as-code (Terraform/Ansible).
Data & ML Tooling
- Familiarity with tools such as MLflow, SageMaker, Kubeflow, Statsig, Airflow, or similar orchestration and experiment-tracking frameworks.
Programming Skills
- Strong proficiency in Python and git; working knowledge of SQL; familiarity with APIs and microservices architectures.
Enterprise Data Platforms
- Experience integrating ML workloads with enterprise data platforms such as Snowflake and transactional systems such as SAP is highly desirable. Familiarity with geospatial data sets.
Operational Mindset
- Strong understanding of reliability, scalability, security, and cost management in production systems.
Collaboration & Communication
- Ability to work effectively with both technical and non-technical stakeholders, translating operational requirements into practical solutions.
Learning Orientation
- Demonstrated curiosity and commitment to staying current with evolving MLOps practices, tools, and AI platform capabilities.
LOCATION
Primary Location
USA-WA-Seattle
SCHEDULE
Full-time
JOB LEVEL
Individual Contributor
JOB TYPE
Experienced
SHIFT
Day (1st)
WHAT WE OFFER
Compensation: This role is eligible for our annual merit-increase program, and we are targeting a salary range of $97,400-$146,000 based on your level of skills, qualifications and experience. You will also be eligible for our Annual Incentive Program, which offers a cash bonus targeting 10% of base pay. Potential plan funding may range from zero to two times that target.
Benefits: When you join our team, you and your dependents will be offered coverage under our comprehensive employee benefits plan, which includes medical, dental, vision, short and long-term disability, and life insurance. We offer a pre-tax Health Savings Account option which includes a company contribution. Other benefit options are also available such as voluntary Long-Term Care and Employee Assistance Programs. We also support personal volunteerism, sponsor a host of diversity networks, promote mentoring, and provide training and development opportunities to help you chart your path to a fulfilling career.
Retirement: Employees are able to enroll in our company’s 401k plan, which includes a paid company match in addition to our contribution equal to 5% of your eligible pay.
Paid Time Off or Vacation: We provide eligible employees who are scheduled to work 25 hours or more per week with 3-weeks of paid vacation to use during your first year of employment. In addition, after being employed for six months, eligible employees begin to accrue vacation for future use. We also recognize eleven paid holidays per year, providing a total of 88 holiday hours and paid parental leave for all full-time employees.
Attention Internal Applicants: To ensure transparency across the organization, please have a discussion with your manager prior to applying for any new opportunities. If you need any help facilitating this conversation, please reach out to your HR Representative for guidance. For more information on how to apply, including best practices for updating your profile or partnering with HR and Recruiting, please visit our internal applicant page on Roots: wy.com/applicants
Weyerhaeuser is an equal opportunity employer. Inclusion is one of our five core values and we strive to maintain a culture where all our people feel a sense of belonging, opportunity and shared purpose. We are committed to recruiting a diverse workforce and supporting an equitable and inclusive environment that inspires people of all backgrounds to join, stay and thrive with our team.
INTRODUCTION
At Weyerhaeuser, we sustainably manage forests and manufacture products that make the world a better place. With a commitment to excellence and innovation, we leverage technology to enhance operational efficiency across timberlands, wood products, and corporate functions. As we continue to scale AI across the enterprise, we are seeking a skilled MLOps Engineer to operationalize machine learning solutions and ensure they are reliable, scalable, secure, and delivering measurable business value in production.
ROLE AND RESPONSIBILITIES
The MLOps Engineer will be responsible for building, deploying, monitoring, and operating machine learning systems across Weyerhaeuser’s AI portfolio, including pricing optimization, industrial AI, geospatial analytics, and generative AI solutions. This role sits at the intersection of data science, software engineering, and cloud infrastructure, enabling the transition from experimental models to trusted, production-grade AI services. You will work closely with data scientists, AI engineers, product managers, and platform teams to implement standardized MLOps patterns that support repeatability, governance, and continuous improvement across the AI lifecycle. The ideal candidate has hands-on experience with ML deployment pipelines, cloud-native infrastructure, model monitoring, and enterprise data platforms—and is motivated by building systems that scale responsibly.
Primary Responsibilities
Operationalize Machine Learning Models
- Design, build, and maintain end-to-end MLOps pipelines that support model training, validation, deployment, and automatic retraining across multiple AI use cases.
Model Deployment & Serving
- Deploy batch and real-time inference workloads using cloud-native services and containerized architectures, ensuring performance, reliability, and cost efficiency.
Monitoring & Observability
- Implement robust monitoring for model performance, data drift, prediction quality, latency, and system health. Establish alerting and diagnostics to support rapid issue detection and remediation.
CI/CD for AI Systems
- Develop and maintain CI/CD workflows for machine learning assets, including code, features, models, and configurations, enabling safe and repeatable releases.
Data & Feature Pipelines
- Collaborate with data engineering teams to ensure reliable data ingestion, feature generation, and versioning to support consistent model behavior across environments. Design, build and support online and offline feature stores.
Governance & Responsible AI
- Support enterprise AI governance by enabling model lineage, reproducibility, auditability, and controlled promotion across environments in alignment with Responsible AI principles.
Cross-Functional Collaboration
- Partner with data scientists, AI engineers, product managers, IT, and cybersecurity teams to architect and implement modeling work into production-ready services.
Platform Enablement
- Contribute to shared MLOps tooling, standards, and reference architectures that accelerate AI delivery across Weyerhaeuser’s AI Factory.
Continuous Improvement
- Identify opportunities to improve reliability, automation, scalability, and developer experience across the AI delivery lifecycle.
BASIC QUALIFICATIONS
Education
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field; advanced degree is a plus.
Experience
- 4-6 years of experience building and supporting production machine learning systems, data platforms, or cloud-native software services in enterprise environments.
MLOps & ML Systems
- Hands-on experience with model lifecycle management, including training pipelines, model registries, deployment strategies, and monitoring.
Cloud & Infrastructure
- Experience with cloud platforms such as AWS or Azure, including containerization (Docker), orchestration (Kubernetes or managed equivalents), and infrastructure-as-code (Terraform/Ansible).
Data & ML Tooling
- Familiarity with tools such as MLflow, SageMaker, Kubeflow, Statsig, Airflow, or similar orchestration and experiment-tracking frameworks.
Programming Skills
- Strong proficiency in Python and git; working knowledge of SQL; familiarity with APIs and microservices architectures.
Enterprise Data Platforms
- Experience integrating ML workloads with enterprise data platforms such as Snowflake and transactional systems such as SAP is highly desirable. Familiarity with geospatial data sets.
Operational Mindset
- Strong understanding of reliability, scalability, security, and cost management in production systems.
Collaboration & Communication
- Ability to work effectively with both technical and non-technical stakeholders, translating operational requirements into practical solutions.
Learning Orientation
- Demonstrated curiosity and commitment to staying current with evolving MLOps practices, tools, and AI platform capabilities.
LOCATION
Primary Location
USA-WA-Seattle
SCHEDULE
Full-time
JOB LEVEL
Individual Contributor
JOB TYPE
Experienced
SHIFT
Day (1st)
WHAT WE OFFER
Compensation: This role is eligible for our annual merit-increase program, and we are targeting a salary range of $97,400-$146,000 based on your level of skills, qualifications and experience. You will also be eligible for our Annual Incentive Program, which offers a cash bonus targeting 10% of base pay. Potential plan funding may range from zero to two times that target.
Benefits: When you join our team, you and your dependents will be offered coverage under our comprehensive employee benefits plan, which includes medical, dental, vision, short and long-term disability, and life insurance. We offer a pre-tax Health Savings Account option which includes a company contribution. Other benefit options are also available such as voluntary Long-Term Care and Employee Assistance Programs. We also support personal volunteerism, sponsor a host of diversity networks, promote mentoring, and provide training and development opportunities to help you chart your path to a fulfilling career.
Retirement: Employees are able to enroll in our company’s 401k plan, which includes a paid company match in addition to our contribution equal to 5% of your eligible pay.
Paid Time Off or Vacation: We provide eligible employees who are scheduled to work 25 hours or more per week with 3-weeks of paid vacation to use during your first year of employment. In addition, after being employed for six months, eligible employees begin to accrue vacation for future use. We also recognize eleven paid holidays per year, providing a total of 88 holiday hours and paid parental leave for all full-time employees.
Attention Internal Applicants: To ensure transparency across the organization, please have a discussion with your manager prior to applying for any new opportunities. If you need any help facilitating this conversation, please reach out to your HR Representative for guidance. For more information on how to apply, including best practices for updating your profile or partnering with HR and Recruiting, please visit our internal applicant page on Roots: wy.com/applicants
Weyerhaeuser is an equal opportunity employer. Inclusion is one of our five core values and we strive to maintain a culture where all our people feel a sense of belonging, opportunity and shared purpose. We are committed to recruiting a diverse workforce and supporting an equitable and inclusive environment that inspires people of all backgrounds to join, stay and thrive with our team.
How to Get Visa Sponsorship as a Mlops Engineer
Target STEM OPT-eligible roles explicitly
MLOps Engineering consistently falls under STEM-designated CIP codes in computer science and engineering. Confirm your degree qualifies before applying, then filter for employers with active E-Verify enrollment, which is required for the 24-month STEM extension.
Emphasize production ML experience over research
Employers sponsoring OPT want engineers who can ship models to production, not just build notebooks. Highlight experience with model serving, monitoring pipelines, and CI/CD for ML workflows. Research background alone raises questions about job-readiness for engineering teams.
Know your OPT authorization timeline before interviewing
Hiring managers often ask when you can start and how long you can work without sponsorship. Know your OPT end date, your STEM extension eligibility window, and your I-20 program end date so you can answer confidently without hesitation.
Prioritize companies with established MLOps infrastructure
Companies already running Kubeflow, MLflow, or Vertex AI pipelines have proven engineering needs for this role. These organizations are far more likely to sponsor because the business case for the hire is already established and headcount is budgeted.
Address sponsorship directly but briefly in applications
Do not hide OPT status or bury it in cover letters. State clearly that you are authorized to work on OPT and eligible for the STEM extension. Ambiguity causes recruiters to pass. Clarity keeps you in the pipeline.
Build a portfolio around real deployment artifacts
GitHub repos showing deployed model pipelines, monitoring dashboards, or containerized inference services carry more weight than academic projects. Sponsors need evidence you can operate at production scale, not just demonstrate theoretical knowledge of MLOps concepts.
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Get Access To All JobsFrequently Asked Questions
Do MLOps Engineer roles qualify for the 24-month STEM OPT extension?
Most do. MLOps Engineering typically falls under STEM-designated fields such as computer science, computer engineering, or data science. Your degree's CIP code determines eligibility, not the job title itself. Confirm your program's CIP code with your DSO before accepting a role, and verify the employer is enrolled in E-Verify, which is a separate requirement for the extension.
How competitive is OPT sponsorship for MLOps Engineer positions specifically?
MLOps is a specialized discipline with a genuine talent shortage, which improves your position relative to more saturated roles like general software engineering. Companies running machine learning in production, particularly in fintech, healthtech, and enterprise SaaS, actively recruit for this skill set. Migrate Mate lists MLOps roles from employers who have demonstrated willingness to work with OPT candidates and file H-1B petitions.
What happens to my OPT authorization if my MLOps role is reclassified or my employer is acquired?
Your OPT authorization is tied to your employment in a role directly related to your degree field, not to a specific employer. If your role changes significantly or your employer undergoes a material change such as an acquisition, notify your DSO promptly. A material change that affects your employment terms may require updating your I-20 or filing an amended H-1B if you have already transitioned to that status.
Can I work as a contractor or through a staffing agency as an MLOps Engineer on OPT?
Contract and staffing arrangements are permitted on OPT, but they carry more scrutiny than direct employment. The work must still be directly related to your degree and meet the minimum 20-hour-per-week requirement. Self-employment is also allowed under OPT rules, though it requires careful documentation. For any arrangement, keep records of your employer of record and the scope of work performed.
What technical skills make an MLOps candidate more likely to receive sponsorship?
Employers prioritizing sponsorship want candidates with demonstrable production experience, specifically containerization with Docker and Kubernetes, experiment tracking tools like MLflow or Weights and Biases, and cloud ML platforms such as AWS SageMaker, GCP Vertex AI, or Azure ML. Candidates who can also write infrastructure-as-code using Terraform or Pulumi stand out. These skills signal that you can operate independently in a production environment from day one.
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