Interested in this AI/ML Engineer role at MD Anderson Cancer Center?
Apply Now →About This Role
The University of Texas MD Anderson Cancer Center is seeking a highly skilled Principal Cybersecurity Analyst to serve as a technical authority within its Cybersecurity department. The Principal Cybersecurity Analyst plays a critical role in shaping and advancing enterprise\-wide governance, risk, and compliance (GRC) programs, with expanded accountability for emerging AI security and governance initiatives. This role operates at a strategic and architectural level while also ensuring operational effectiveness across cybersecurity risk management practices.
The Principal Cybersecurity Analyst contributes directly to safeguarding sensitive data, maintaining regulatory compliance, and strengthening the organization's security posture through innovative, data\-driven risk management and governance strategies. The Principal Cybersecurity Analyst serves as a trusted advisor, architect, and leader within the cybersecurity function.
The ideal candidate brings advanced education in information systems or cybersecurity, extensive experience leading enterprise risk management or GRC programs, and strong knowledge of frameworks such as NIST, HIPAA, and HITRUST. Preferred candidates will also have hands\-on experience assessing AI/ML risk, strong executive communication skills, and certifications such as CISSP, CISM, or PMP.
Minimum $123,000 \- Midpoint $154,000 \- Maximum $185,000
Work Location: Remote 100%
Why Us?
At UT MD Anderson, the Principal Cybersecurity Analyst plays an essential role in advancing cybersecurity innovation in a mission\-driven healthcare environment. This position offers the opportunity to shape enterprise risk strategy, influence executive decision\-making, and lead emerging AI governance practices, all while supporting an organization dedicated to saving lives. Employees benefit from a collaborative culture, professional development opportunities, and a strong commitment to work\-life balance.
- 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
Governance, Risk \& Compliance (GRC) Architecture \& Risk Management Program Leadership
- Serve as principal architect and subject matter expert for enterprise GRC and cybersecurity risk programs
- Define and maintain risk assessment methodologies, control frameworks, and risk scoring models
- Establish workflows across development, staging, and production environments
- Develop SOPs, roles, segregation of duties, and change management processes
- Lead design and execution of enterprise risk management strategies
Security \& Risk Platform Integration and Automation
- Architect and maintain integrations across Archer, Tenable, ServiceNow, Microsoft Configuration Manager (SCCM/MECM), and Medigate
- Design automated workflows consolidating asset, vulnerability, and risk data
- Enable enterprise\-wide risk visibility and reporting through data\-driven systems
- Ensure data quality, reconciliation, and interoperability across platforms and CMDB
Regulatory \& Compliance Framework Management
- Apply frameworks including NIST CSF, NIST 800\-53, HIPAA, and HITRUST
- Conduct control mapping, gap assessments, and compliance reporting
- Advise stakeholders on remediation strategies and regulatory requirements
- Align institutional policies with regulatory and accreditation standards
AI Security \& Governance
- Establish and mature AI security and governance programs
- Develop AI risk assessment criteria and governance standards
- Align controls to NIST AI Risk Management Framework and institutional policies
- Evaluate AI/ML tools and vendors for data protection and compliance risks
- Partner with data governance, privacy, and legal teams on AI initiatives
Strategic Risk Visioning, Assessment \& Executive Advisory
- Develop multi\-year roadmaps, milestones, and resource plans
- Lead enterprise and project\-level risk assessments
- Translate technical findings into executive\-level reporting and dashboards
- Advise leadership on risk posture and investment prioritization
- Provide technical leadership and mentorship across teams
EDUCATION
- Required: Bachelor's Degree Computer Information Systems, Business Information Systems, Computer Science or related field.
- Preferred: Master's Degree Information Security, Computer Science or related field
WORK EXPERIENCE
- Required: 7 years In information security and/or cybersecurity experience including multiple security domains, to include two years lead/supervisory experience.
- May substitute required education degree with additional years of equivalent experience on a one to one basis.
- Preferred: At least 7\-8 years of progressive cybersecurity experience, hands\-on risk management (risk assessment, risk register ownership, control evaluation, or risk quantification), led or owned a security risk management program or framework implementation (e.g., NIST RMF/CSF, ISO 27005, FAIR, or equivalent), direct hands\-on experience assessing the security or risk posture of AI/ML systems or GenAI deployments, ability to translate technical risk into business\-level language for executive and governance audiences (e.g., briefing a CISO, risk committee, or board), experience using Archer, excellent communication skills, risk management, and cybersecurity framework is a must.
LICENSES AND CERTIFICATIONS
- Preferred: CISSP \- Cert IS Security Professional Issued by the International Information Systems Security Certification Consortium ((ISC).
- Preferred: CISM \- Cert Info Security Manager Issued by Information Systems Audit and Control Association (ISACA).
- Preferred: PMP \- Project Mgt Professional Issued by the Project Management Institute (PMI).
- Preferred: Other applicable security industry certifications.
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.http://www.mdanderson.org/about\-us/legal\-and\-policy/legal\-statements/eeo\-affirmative\-action.html
Additional Information
- Requisition ID: 181706
- Employment Status: Full\-Time
- Employee Status: Regular
- Work Week: Days
- Minimum Salary: US Dollar (USD) 123,000
- Midpoint Salary: US Dollar (USD) 154,000
- Maximum Salary : US Dollar (USD) 185,000
- 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
Salary Context
This $123K-$185K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At MD Anderson Cancer Center, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($154K) sits 30% below the category median. Disclosed range: $123K to $185K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
MD Anderson Cancer Center AI Hiring
MD Anderson Cancer Center has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US. Compensation range: $185K - $185K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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