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About Ellucian:
Ellucian powers innovation for higher education, partnering with approximately 3,000 customers across 50 countries, serving more than 21 million students. Ellucian's AI\-powered platform, trained on the richest dataset available in higher education, drives efficiency, personalized experiences, and strengthened engagement for all students, faculty and staff. Fueled by decades of experience with a singular focus on the unique needs of learning institutions, the Ellucian platform features best\-in\-class SaaS capabilities and delivers insights needed now and into the future. These solutions and services span the entire student lifecycle, including data\-rich tools for student recruitment, enrollment, and retention to workforce analytics, fundraising, and alumni engagement. Ellucian's innovative solutions, vast ecosystem of partners and user community of more than 45,000 provides best practices leading to greater institutional success and achieving better student outcomes.
About the Opportunity:
Ellucian is seeking a strategic, collaborative, and business\-oriented Assistant General Counsel, Privacy \& AI Governance to join our Legal team. Reporting to the Deputy General Counsel, Compliance, this role serves as the company's lead legal advisor on global privacy, data protection, AI governance, and data ethics, partnering with Product, Engineering, Information Security, IT, HR, and executive leadership to enable innovation while managing legal and regulatory risk.
The successful candidate will lead Ellucian's global privacy legal strategy, help shape its AI governance framework, and provide practical legal guidance on privacy, cybersecurity, AI\-enabled technologies, cross\-border data transfers, and emerging regulations. This is a fantastic opportunity to influence enterprise strategy and help position Ellucian as a trusted leader in responsible technology innovation.
Where you will make an impact
Privacy \& Data Protection
- Serve as Ellucian's lead legal advisor on global privacy, data protection, and cross\-border data transfers, providing practical guidance on privacy\-by\-design, customer commitments, and compliance with evolving global regulations.
- Monitor emerging privacy laws and translate complex legal requirements into practical guidance that enables the business to innovate responsibly.
- Advise business stakeholders on privacy\-by\-design principles throughout the product lifecycle.
AI Governance \& Emerging Technology
- Lead legal support for Ellucian's AI governance program, advising on AI, generative AI, machine learning, and other emerging technologies.
- Shape governance frameworks and counsel business leaders on responsible AI practices, including transparency, intellectual property, bias, accountability, and evolving regulatory requirements.
- Counsel stakeholders regarding AI transparency, accountability, intellectual property, bias, explainability, and responsible AI deployment.
- Monitor evolving AI legislation, including the EU AI Act and other global regulatory developments, and develop practical guidance for business leaders.
Cybersecurity, Data Governance \& Risk Management
- Advise on cybersecurity, incident response, data governance, and legal obligations related to personal and sensitive information, partnering closely with Information Security and IT.
- Assess legal risks associated with new products, technologies, and strategic initiatives while developing policies and guidance that strengthen enterprise compliance.
- Support enterprise data governance initiatives involving data ownership, data classification, retention, and responsible data use.
Strategic Business Partnership
- Partner with Product, Engineering, Information Security, IT, HR, Sales, Procurement, and executive leadership to enable innovation while managing legal and regulatory risk.
- Advise on technology transactions, AI\-enabled products, customer commitments, vendor relationships, and other strategic business initiatives.
- Support strategic transactions involving privacy, data sharing, technology licensing, AI\-enabled products, and vendor relationships.
Leadership \& Operational Excellence
- Lead the continued evolution of Ellucian's global privacy legal program and AI governance framework, ensuring scalable and effective governance.
- Develop scalable legal processes, manage outside counsel, and contribute to Legal team initiatives that drive operational excellence.
What you will bring
- Juris Doctor (JD) from an accredited law school and active membership in at least one U.S. state bar.
- 10\+ years of legal experience with expertise in privacy, data protection, cybersecurity, technology, or regulatory law.
- Strong knowledge of global privacy regulations, including GDPR, UK GDPR, CCPA/CPRA, FERPA, and other international privacy frameworks.
- Experience advising SaaS, cloud, software, or technology companies on privacy, data governance, cybersecurity, and AI\-related legal matters.
- Strong understanding of AI governance, emerging technology regulation, cybersecurity, and data governance principles, including the EU AI Act.
- Proven ability to partner with cross\-functional business and technical leaders while exercising excellent judgement to balance legal risk with business objectives.
- Excellent drafting, negotiation, communication, and stakeholder management skills, with the ability to translate complex legal concepts into practical business guidance.
Extra credit
- Significant in\-house legal experience supporting a global SaaS, cloud, software, or technology company.
- Experience building scalable; enterprise\-wide privacy compliance, AI governance \& Data governance programs and processes.
- CIPP/US, CIPP/E, CIPM, or other IAPP certification.
- Experience supporting global operations and cross\-border privacy matters.
- Familiarity with higher education technology or highly regulated technology environments
What makes \#Ellucianlife:
- Comprehensive health coverage: medical, dental, and vision
- Flexible time off
- Thrive Flex Lifestyle Account (LSA) that allows you to contribute towards your health, financial or learning interests
- 401k w/ match \& BrightPlan \- to help you save for the future
- Parental Leave
- 5 charitable days to support the community that supports us
- Telemedicine
- Wellness
+ Headspace Care (mental health)
+ Wellbeats (virtual fitness classes)
- RethinkCare \& Wellthy– caregiver support
- Diversity and inclusion programs which provide access to internal employee resource groups
- Employee referral bonuses to encourage the addition of great new people to the team
- We Foster a learning culture with:
+ Education Assistance Program
+ Professional development opportunities
+ LinkedIn Learning
\#LI\-JG1
\#LI\-Hybrid
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 Ellucian, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Ellucian AI Hiring
Ellucian has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US.
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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