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About This Role
Overall Position Description:
World Food Program USA mobilizes American policymakers, businesses and individuals to support the World Food Programme (WFP) in its mission to end hunger. The Vice President, Technology, Innovation and AI will help transform how the organization works, turning technology and innovation into greater capacity, stronger decisions and measurable mission impact.
Reporting to the Chief Operations Officer and Chief Financial Officer, the Vice President leads the IT, CRM and Data Analytics teams and advances the strategic plan by equipping staff with the tools, skills and trusted data to work more effectively. This leader sets and implements an enterprise technology vision, scales integrated data capabilities and embeds useful, responsible AI into everyday work. Success will be demonstrated through sustained staff adoption, measurable return on investment and reliable data integration that improves decisions and donor engagement.
The Vice President combines strategic leadership, business judgment and technical depth. They stay close enough to the work to evaluate architecture, challenge assumptions and guide consequential decisions, stepping in directly when needed. They deliver primarily through team leaders, staff and vendors, translating business needs into effective solutions and presenting progress, opportunities and risks to executive leadership and the Board as appropriate.
General Responsibilities
Turn strategy into measurable value
- Develop and execute an enterprise technology, data and AI roadmap aligned with the strategic plan. Partner with department leaders to prioritize investments, sequence delivery and establish shared accountability for outcomes.
- Build business cases with clear baselines, full implementation and operating costs, expected benefits and adoption measures. Track realized ROI with Finance and business owners, distinguishing financial savings from capacity gains and service improvements.
- Set enterprise technology investment priorities and oversee the technology budget and vendor portfolio within delegated authority. Empower team leaders to manage delivery and assigned budgets. Partner with departments that hold technology budgets and ensure all technology selection, purchasing and implementation follows organizational processes, standards and protocols.
Make AI work for people
- Lead substantial AI initiatives from opportunity assessment through deployment and sustained use across functions. Select practical use cases, test value and reliability, and scale solutions that demonstrate measurable benefits.
- Partner with department leaders and People and Culture to build AI literacy and staff confidence through training, coaching, peer champions and hands-on demonstrations. Address barriers and use staff feedback to improve adoption.
- Embed change management in every major initiative, including stakeholder engagement, workflow redesign, communications, training and post-launch support. Measure effective use and business outcomes beyond licenses issued or training completed.
- Create a culture of thoughtful experimentation and continuous learning. Apply sound build-versus-buy judgment and guide teams and vendors toward maintainable solutions that fit organizational capacity.
Connect data to decisions
- Lead the continued development and integration of enterprise platforms through the CRM and Data Analytics leaders, who own their respective platform roadmaps and delivery. Align their work to provide a secure, trusted view of donors and organizational performance.
- Enable the Data Analytics team to scale reliable warehouse capabilities, data pipelines and integrations. Establish shared expectations for data quality, documentation and governance, with business stakeholders accountable for definitions and validation.
- Partner with Development, Marketing and Communications, Public Policy, Finance and other teams to translate operational needs into useful analytics, dashboards, automation and AI-enabled workflows.
- Set enterprise architecture principles and guide major platform and integration decisions. Establish one accountable technical owner for each integration across delivery, monitoring and ongoing support; resolve ownership and priority conflicts among IT, CRM and Data Analytics.
Build trust in technology
- Lead and empower functional leaders to deliver reliable technology services and platforms. Set service expectations, cybersecurity and continuity priorities, and escalation thresholds; review performance across IT, CRM and Data Analytics and remove barriers to delivery.
- Lead enterprise technology risk management, with IT responsible for shared infrastructure, identity and access, cybersecurity controls and incident readiness, and CRM and Data Analytics responsible for controls within their platforms. Escalate material risks and investment needs to the COO/CFO and relevant organizational leaders.
- Establish and maintain best practices for technology, data and AI governance, including policy development, clear standards and decision authority, and monitoring of adherence and effectiveness. Partner with department leaders and relevant advisers to keep governance practical and responsive to evolving needs.
Grow talent and lead together
- Attract, hire, develop and retain strong technical talent. Lead the leaders of IT, CRM and Data Analytics, organizing work around shared priorities and complementary strengths. Coach managers, support succession and hold teams accountable for delivery and service quality.
- Build on the strengths of current teams, support professional growth and recommend how capabilities, capacity and ways of working can best advance the strategy.
- Build trust across departments by listening to business needs, explaining tradeoffs clearly and resolving competing priorities with the COO/CFO and other leaders. Establish proactive staff communication about technology changes and disruptions, including why changes are happening, expected impact, timing, preparation and support. Ensure timely status updates and expected resolution or next-update times during disruptions.
Qualifications
Education
- Bachelor’s degree or equivalent combination of education and relevant professional experience required.
- Master’s degree in information systems, technology management, computer science, business administration or a related field preferred.
Years of experience
- At least 15 years of progressively responsible relevant professional experience, including at least eight years of people management experience.
Specific qualifications
- Enterprise technology leadership spanning strategy, budgets, vendors, operations and the development of technical teams and managers.
- Proven leadership of at least one substantial AI implementation that moved into regular business use, achieved widespread and sustained adoption, and produced measurable organizational results. Experience must extend beyond pilots and individual tool use.
- Proven change-management leadership across departments, including stakeholder engagement, staff enablement, workflow change and measurement of sustained adoption.
- Experience leading enterprise data warehouse implementation or modernization, integration and governance to provide reliable, accessible business information.
- Deep technical judgment applied close to the work: evaluate architecture and integration choices, challenge vendor proposals and recognize when implementation needs intervention. Guide teams effectively and contribute directly when needed.
- Sound business judgment: weigh mission impact, cost, risk, staff capacity and competing priorities; make timely decisions and assess whether investments deliver their intended value.
- Ability to influence across departments, explain complex subjects clearly and build relationships with colleagues, executives and Board members.
- Demonstrated ability to hire, develop and organize technical talent, including leading managers and building collaboration across disciplines. Lead with curiosity, accountability, inclusion and respect.
- Experience leading the implementation and ongoing oversight of Microsoft Purview, including data classification, retention, data loss prevention, eDiscovery and related compliance controls.
- Highly professional collaborator with personal integrity and discretion in handling confidential information; adaptable and comfortable with ambiguity; strong leadership presence and ability to influence others without direct authority.
Preferred qualifications
- Substantial Salesforce experience, particularly CRM strategy, integration and adoption. Experience with Snowflake or a comparable enterprise data platform is also valuable.
- Experience in a nonprofit or mission-driven organization, with familiarity with fundraising, donor engagement or advocacy operations.
- Relevant professional certification in change management, enterprise technology, data architecture or information security. Credentials complement demonstrated delivery and adoption experience.
Location and work arrangement
This position is based in the Washington, DC office and requires a minimum of three days per week in the office. Candidates must reside in the Washington, DC, Maryland or Virginia metropolitan area by their start date. Candidates willing to relocate are welcome; relocation assistance is not available.
Salary Context
This $187K-$192K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At World Food Program USA, 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 Required
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 $211,950 based on 13,048 positions with disclosed compensation. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $187K to $192K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
World Food Program USA AI Hiring
World Food Program USA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $192K - $192K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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