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JOB POSTING
Job Title: Senior Manager, Data Privacy & AI Governance
Department: Risk, Compliance and Legal
Reports to: Chief Risk Officer, General Counsel
Position Summary
The Senior Manager, Data Privacy & AI Governance is responsible for leading the organization’s data protection strategy and governance of artificial intelligence in support of our mission to enable safe, effective, and impactful cross-border philanthropy. This role safeguards the personal data of donors, charity partners, and stakeholders worldwide, while developing and maintaining policies, governance structures, and controls for the responsible, ethical, and compliant use of AI across the organization.
This individual will design, implement, and embed privacy and AI governance policies that enable CAF America to operate confidently across jurisdictions, adopt emerging technologies responsibly, and protect sensitive data integral to the trust and transparency we uphold in our philanthropic work. The role will work closely with Information Technology to align AI governance with cybersecurity, data management, vendor management, system implementation, and operational risk controls.
Responsibilities:
- Develop, maintain, and embed data privacy and governance policies, standards, and procedures in alignment with CAF America’s Policy Framework and international philanthropic operations.
- Translate multi-jurisdictional legal and regulatory privacy requirements into clear, business-friendly policies that support our work with donors and charitable organizations worldwide.
- Establish policy review cycles to ensure documents remain current with evolving laws, regulations, and best practices in the global nonprofit sector.
- Lead CAF America’s governance of artificial intelligence by developing organization-wide AI policies, governance structures, approval pathways, and oversight processes that support responsible innovation and risk management.
- Maintain an inventory of AI use cases, tools, and related data flows, and establish review standards for evaluating privacy, security, operational, legal, ethical, and reputational risks before AI tools are adopted or materially changed.
- Monitor, assess, and respond to international regulatory developments affecting data privacy, cross-border data flows, and nonprofit compliance.
- Conduct horizon scanning for relevant changes in charity law, data protection, and emerging donor privacy standards.
- Guide business units in integrating privacy requirements into cross-border giving processes, due diligence, and international partner engagement.
- Partner with internal teams, Philanthropy Services, Grants, IT, Marketing, Legal—to ensure privacy practices are embedded into global program operations, donor communications, and partner onboarding.
- Work closely with Information Technology to align AI governance with technology architecture, cybersecurity controls, data access standards, vendor review, system implementation, and ongoing monitoring of approved AI tools.
- Facilitate policy consultations with document owners, subject matter experts, and regional counterparts to ensure alignment between regulatory compliance and operational realities in different countries.
- Engage senior leadership to ensure privacy considerations are built into strategic decisions and new initiatives.
- Oversee governance and approval processes for privacy-related deviations or waivers, ensuring documentation and compliance controls are maintained.
- Support the design and delivery of organization-wide privacy training tailored to the philanthropic and nonprofit context.
- Support the design and delivery of AI literacy, responsible AI, and privacy-by-design training for staff, with practical guidance on approved AI uses, restricted data, human oversight, and escalation procedures.
- Contribute to second-line compliance monitoring and readiness for audits by regulators, partners, and accreditation bodies.
- Represent CAF America in privacy-related working groups, regulatory forums, and sector-wide initiatives on ethical data stewardship in philanthropy.
- Act as a deputy for the General Counsel & Chief Risk Officer, when required, at internal and external events.
Education and Experience* Must be eligible to work legally in the United States.
- Bachelor’s degree required; advanced degree in Law, Information Security, Public Policy, or related field strongly preferred.
- 7+ years of relevant experience in data privacy, compliance, technology governance, AI governance, or risk management, preferably within international nonprofit, philanthropic, or NGO settings.
- Ability to work and prioritize in a fast-paced and rapidly changing environment.
- Superior attention to detail and ability to accurately navigate large and detailed databases.
- Excellent written and oral communications skills; ability to adjust communications according to the audience.
- Professional privacy certification (e.g., CIPP/E, CIPP/US, CIPM)is a plus.
- Demonstrated ability to develop AI governance policies, risk assessment processes, oversight structures, and practical controls for the responsible use of emerging technologies.
- In-depth understanding of global data protection laws and their application in cross-border nonprofit operations.
- Proven ability to develop and embed policies in complex, multi-stakeholder environments.
- Exceptional communication, facilitation, and relationship management skills, with the ability to work across cultures and time zones
This position is U.S.-based, and all duties must be carried out from within the United States. To perform this job successfully, an individual must be able to perform each essential job duty satisfactorily, with or without accommodation. If you require accommodation to perform these essential functions, please contact the Human Resources Department. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Your health and wellness are important to us. CAF America offers a comprehensive benefits package which includes 100% paid premium for Employee Medical, Dental and Vision insurance along with a Health Savings Account (HSA), Life Insurance, Accidental Death and Dismemberment Insurance, Short- and Long-Term Disability, 401k program with up to 15% match, Employee Assistance Program (EAP) and robust Time Off programs.
Hiring Salary Range: $125,000 - $132,000
Salary Context
This $125K-$132K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CAF America, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($128K) sits 39% below the category median. Disclosed range: $125K to $132K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
CAF America AI Hiring
CAF America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $132K - $132K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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