MD Anderson Cancer Center E-3 Visa Sponsorship Jobs USA
University of Texas MD Anderson Cancer Center sponsors E-3 visas for Australian professionals in clinical, research, and administrative roles. As one of the world's leading cancer research institutions, MD Anderson offers meaningful opportunities for specialists in healthcare, life sciences, and data-driven research to build careers in the U.S.
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Get Access To All JobsINTRODUCTION
The University of Texas MD Anderson Cancer Center is seeking a Senior Machine Learning Operations Engineer to support enterprise-wide artificial intelligence initiatives within Data Impact & Governance. The Senior Machine Learning Operations Engineer will join a multidisciplinary environment that integrates multidimensional data, advanced analytics, and machine learning to drive sustainable, responsible AI solutions that improve cancer care outcomes.
Within this mission-driven environment, the Senior Machine Learning Operations Engineer plays a critical role in building, deploying, and sustaining production-quality machine learning systems. The Senior Machine Learning Operations Engineer partners closely with data scientists, engineers, clinicians, and business stakeholders to ensure AI solutions are scalable, secure, reliable, and aligned with responsible AI principles across UT MD Anderson.
The ideal candidate is a seasoned machine learning or software engineering professional with a strong foundation in MLOps, cloud and on-premises AI platforms, and healthcare-focused AI lifecycle management. This individual typically holds a Bachelor's degree in a relevant technical discipline, with a Master's degree preferred, and brings significant hands-on experience developing, deploying, and maintaining machine learning systems in production environments. Experience leading or designing shared ML services, evaluating third-party AI solutions, and applying responsible AI practices within regulated or clinical settings is highly valued.
Minimum $146,500 - Midpoint $183,000 - Maximum $219,500 based on a 40-hour work week.
Work Location: Remote within Texas only.
WHY US?
This role offers the opportunity to directly influence how artificial intelligence is responsibly scaled across UT MD Anderson, contributing to meaningful, long-lasting improvements in cancer care while working alongside experts in data science, engineering, and clinical innovation. The Senior Machine Learning Operations Engineer is supported by an environment that values continuous learning, technical excellence, and sustainable work practices while enabling professional growth and enterprise-level impact.
- Employer-paid medical coverage starting day one for employees working 30+ hours/week, plus optional group dental, vision, life, AD&D, and disability insurance.
- Accruals for PTO and Extended Illness Bank, plus paid holidays, wellness, childcare, and other leave options.
- Tuition Assistance Program after six months of service and access to extensive wellness, fitness, and employee resource groups.
- Defined-benefit pension through the Teachers Retirement System, voluntary retirement plans, and employer-paid life and reduced salary protection programs.
Responsibilities
AI Model Lifecycle & MLOps
- Oversee end-to-end AI model lifecycles including training, evaluation, deployment, monitoring, and maintenance of production-quality machine learning models
- Design and implement CI/CD pipelines for model training, deployment, monitoring, and retraining with a focus on security, scalability, reliability, reproducibility, and performance
- Implement rigorous testing, versioning, and documentation practices to support reproducibility, risk mitigation, and measurable impact
- Maintain comprehensive experiment tracking, data lineage, model lineage, and model scorecards
- Design fallback, rollback, and decommissioning strategies to ensure operational continuity of AI solutions
Responsible AI & Governance
- Promote responsible AI practices by minimizing bias, enhancing fairness, and maximizing transparency in machine learning models
- Ensure AI lifecycle management aligns with institutional standards and best practices
- Support assessment, validation, and onboarding of external machine learning models and AI-driven products to minimize organizational risk and maximize value
Platform, Infrastructure & Tooling
- Develop and maintain scalable data pipelines, feature stores, and artifact management systems
- Deploy and operate ML workloads across cloud and on-premises environments including Azure, AWS, or GCP
- Utilize containerization and orchestration technologies such as Docker, Kubernetes, and DAG-based tools
- Apply DevOps and MLOps tools including Azure DevOps, GitHub Actions, and version control systems
Stakeholder Engagement & Enablement
- Collaborate with stakeholders to gather requirements, translate AI concepts into understandable terms, and incorporate feedback
- Partner with data scientists, ML engineers, and software engineers to integrate models into enterprise systems
- Deliver training and knowledge sharing to enhance AI understanding and adoption across the organization
- Report project progress, impact, risks, and recommendations to leadership
Innovation & Continuous Learning
- Stay current with emerging technology trends in AI, MLOps, and healthcare analytics
- Contribute to internal and external technical communities
- Foster a culture of continuous improvement, innovation, and learning across teams
- Perform other duties as assigned
MINIMUM QUALIFICATIONS
Education Required: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
Preferred Education: Master's Level Degree
Experience Required: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With Master's degree, three years' experience required. With PhD, one year of experience required.
Preferred Experience: Experience developing MLOps pipelines for computer vision AI models, hands-on experience developing custom machine learning algorithms from scratch (e.g., in NumPy or PyTorch), designed and implemented shared machine learning service that is used across multiple teams or production projects, led the development of systems that automate the deployment and maintenance of multiple machine learning models into user-facing products, five years of industry experience in data science, with at least 3 of those years as a Senior Machine Learning Engineer
The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law.
ADDITIONAL INFORMATION
- Requisition ID: 180720
- Employment Status: Full-Time
- Employee Status: Regular
- Work Week: Days
- Minimum Salary: US Dollar (USD) 146,500
- Midpoint Salary: US Dollar (USD) 183,000
- Maximum Salary: US Dollar (USD) 219,500
- FLSA: exempt and not eligible for overtime pay
- Fund Type: Hard
- Work Location: Remote (within Texas only)
- Pivotal Position: Yes
- Referral Bonus Available?: Yes
- Relocation Assistance Available?: Yes
LI-Remote
INTRODUCTION
The University of Texas MD Anderson Cancer Center is seeking a Senior Machine Learning Operations Engineer to support enterprise-wide artificial intelligence initiatives within Data Impact & Governance. The Senior Machine Learning Operations Engineer will join a multidisciplinary environment that integrates multidimensional data, advanced analytics, and machine learning to drive sustainable, responsible AI solutions that improve cancer care outcomes.
Within this mission-driven environment, the Senior Machine Learning Operations Engineer plays a critical role in building, deploying, and sustaining production-quality machine learning systems. The Senior Machine Learning Operations Engineer partners closely with data scientists, engineers, clinicians, and business stakeholders to ensure AI solutions are scalable, secure, reliable, and aligned with responsible AI principles across UT MD Anderson.
The ideal candidate is a seasoned machine learning or software engineering professional with a strong foundation in MLOps, cloud and on-premises AI platforms, and healthcare-focused AI lifecycle management. This individual typically holds a Bachelor's degree in a relevant technical discipline, with a Master's degree preferred, and brings significant hands-on experience developing, deploying, and maintaining machine learning systems in production environments. Experience leading or designing shared ML services, evaluating third-party AI solutions, and applying responsible AI practices within regulated or clinical settings is highly valued.
Minimum $146,500 - Midpoint $183,000 - Maximum $219,500 based on a 40-hour work week.
Work Location: Remote within Texas only.
WHY US?
This role offers the opportunity to directly influence how artificial intelligence is responsibly scaled across UT MD Anderson, contributing to meaningful, long-lasting improvements in cancer care while working alongside experts in data science, engineering, and clinical innovation. The Senior Machine Learning Operations Engineer is supported by an environment that values continuous learning, technical excellence, and sustainable work practices while enabling professional growth and enterprise-level impact.
- Employer-paid medical coverage starting day one for employees working 30+ hours/week, plus optional group dental, vision, life, AD&D, and disability insurance.
- Accruals for PTO and Extended Illness Bank, plus paid holidays, wellness, childcare, and other leave options.
- Tuition Assistance Program after six months of service and access to extensive wellness, fitness, and employee resource groups.
- Defined-benefit pension through the Teachers Retirement System, voluntary retirement plans, and employer-paid life and reduced salary protection programs.
Responsibilities
AI Model Lifecycle & MLOps
- Oversee end-to-end AI model lifecycles including training, evaluation, deployment, monitoring, and maintenance of production-quality machine learning models
- Design and implement CI/CD pipelines for model training, deployment, monitoring, and retraining with a focus on security, scalability, reliability, reproducibility, and performance
- Implement rigorous testing, versioning, and documentation practices to support reproducibility, risk mitigation, and measurable impact
- Maintain comprehensive experiment tracking, data lineage, model lineage, and model scorecards
- Design fallback, rollback, and decommissioning strategies to ensure operational continuity of AI solutions
Responsible AI & Governance
- Promote responsible AI practices by minimizing bias, enhancing fairness, and maximizing transparency in machine learning models
- Ensure AI lifecycle management aligns with institutional standards and best practices
- Support assessment, validation, and onboarding of external machine learning models and AI-driven products to minimize organizational risk and maximize value
Platform, Infrastructure & Tooling
- Develop and maintain scalable data pipelines, feature stores, and artifact management systems
- Deploy and operate ML workloads across cloud and on-premises environments including Azure, AWS, or GCP
- Utilize containerization and orchestration technologies such as Docker, Kubernetes, and DAG-based tools
- Apply DevOps and MLOps tools including Azure DevOps, GitHub Actions, and version control systems
Stakeholder Engagement & Enablement
- Collaborate with stakeholders to gather requirements, translate AI concepts into understandable terms, and incorporate feedback
- Partner with data scientists, ML engineers, and software engineers to integrate models into enterprise systems
- Deliver training and knowledge sharing to enhance AI understanding and adoption across the organization
- Report project progress, impact, risks, and recommendations to leadership
Innovation & Continuous Learning
- Stay current with emerging technology trends in AI, MLOps, and healthcare analytics
- Contribute to internal and external technical communities
- Foster a culture of continuous improvement, innovation, and learning across teams
- Perform other duties as assigned
MINIMUM QUALIFICATIONS
Education Required: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline.
Preferred Education: Master's Level Degree
Experience Required: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With Master's degree, three years' experience required. With PhD, one year of experience required.
Preferred Experience: Experience developing MLOps pipelines for computer vision AI models, hands-on experience developing custom machine learning algorithms from scratch (e.g., in NumPy or PyTorch), designed and implemented shared machine learning service that is used across multiple teams or production projects, led the development of systems that automate the deployment and maintenance of multiple machine learning models into user-facing products, five years of industry experience in data science, with at least 3 of those years as a Senior Machine Learning Engineer
The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law.
ADDITIONAL INFORMATION
- Requisition ID: 180720
- Employment Status: Full-Time
- Employee Status: Regular
- Work Week: Days
- Minimum Salary: US Dollar (USD) 146,500
- Midpoint Salary: US Dollar (USD) 183,000
- Maximum Salary: US Dollar (USD) 219,500
- FLSA: exempt and not eligible for overtime pay
- Fund Type: Hard
- Work Location: Remote (within Texas only)
- Pivotal Position: Yes
- Referral Bonus Available?: Yes
- Relocation Assistance Available?: Yes
LI-Remote
Job Roles at MD Anderson Cancer Center
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Get Access To All JobsTips for Finding MD Anderson Cancer Center E-3 Visa Sponsorship Jobs USA
Match your degree to the role
MD Anderson's E-3 roles in clinical research, bioinformatics, and healthcare administration require a degree in a directly related field. A nursing degree won't support a data science application. Review the specialty occupation definition through USCIS before you apply to confirm your credential aligns.
Target research and clinical departments directly
MD Anderson's hiring for E-3 sponsorship tends to concentrate in oncology research, translational science, and patient care units. Applying through department-specific postings rather than general job listings puts your application in front of hiring managers who already understand visa sponsorship needs.
Verify the prevailing wage before negotiating
Your offer salary must meet the DOL prevailing wage for your role and Houston location. Use the OFLC Wage Search to look up the correct wage level before your offer conversation so you can confirm compliance without surprises during the LCA filing stage.
Clarify LCA timing with HR early
MD Anderson's HR and immigration teams must certify the Labor Condition Application with DOL before your visa appointment. Ask your recruiter which internal team manages E-3 filings and confirm the LCA can be filed ahead of your target consulate date, not after.
Use Migrate Mate to prepare your filing documents
Once MD Anderson issues your offer letter, use Migrate Mate to prepare your E-3 application package. Migrate Mate assigns a dedicated E-3 visa expert to handle the paperwork, so your DS-160, support letter, and LCA arrive at the consulate in the correct format.
Prepare your specialty occupation evidence carefully
Consular officers reviewing E-3 applications for research or clinical roles at academic medical centers sometimes request additional documentation on how your degree connects to the position. Bring your official transcripts, your job offer letter, and an O*NET occupation profile printout to your interview.
MD Anderson Cancer Center E-3 Visa Sponsorship: Frequently Asked Questions
Does University of Texas MD Anderson Cancer Center sponsor E-3 visas?
Yes, MD Anderson sponsors E-3 visas for qualified Australian nationals in eligible specialty occupation roles. The institution has an established immigration support process through its HR department and works with legal teams experienced in E-3 filings. Sponsorship is tied to specific roles, so confirming visa support before accepting an offer is essential.
Which roles and departments at MD Anderson typically receive E-3 sponsorship?
E-3 sponsorship at MD Anderson is most common in clinical research, bioinformatics, translational science, nursing, and healthcare administration. These roles require a bachelor's degree or higher in a directly related field, which satisfies the specialty occupation requirement. Roles in general support functions or operations are less likely to qualify under the E-3 definition.
How do I navigate the E-3 application process at MD Anderson?
After receiving your offer letter, MD Anderson's HR team coordinates the Labor Condition Application filing with DOL. Once the LCA is certified, you complete the DS-160, pay the MRV fee, and attend your consulate interview in Australia. Migrate Mate can prepare your full application package and connect you with an E-3 expert to guide each step.
How long does the E-3 sponsorship process take at MD Anderson?
The DOL typically certifies the LCA within seven business days of submission. Consulate appointment availability in Sydney, Melbourne, or Perth then determines your overall timeline, and wait times fluctuate throughout the year. Most candidates should plan for three to six weeks between receiving their offer letter and attending their visa interview, depending on consulate scheduling.
What should I ask MD Anderson's HR team about E-3 sponsorship before accepting an offer?
Confirm that the specific role is classified as a specialty occupation under USCIS guidelines and that MD Anderson will file the LCA on your behalf before your target start date. Ask whether the institution uses an in-house immigration team or outside counsel, and clarify who your point of contact will be throughout the E-3 filing process.
What is the prevailing wage for E-3 sponsorship at University of Texas M D Anderson Cancer Center?
E-3 employers must pay at least the prevailing wage, determined when they file a Labor Condition Application with the Department of Labor. The rate is based on the role, location, and experience level, ensuring Australian hires are paid comparably to U.S. workers in the same position. You can look up prevailing wage rates for any occupation and location using the DOL's OFLC Wage Search tool.