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Senior Digital Specialist – Trustworthy AI (with Cybersecurity)
Job #: req38547
Organization: World Bank Group
Sector: Digital Development
Grade: GG
Term Duration: Open-Ended Staff (OPEN-OP)
Recruitment Type: International Recruitment
Location: Washington, DC,United States
Required Language(s): English
Preferred Language(s):
Closing Date: 10/16/2026 (MM/DD/YYYY) at 11:59pm UTC
Description
Do you want to build a career that is truly worthwhile? The World Bank Group is a unique global partnership of five institutions driven by a bold vision to create a world free of poverty on a livable planet. As one of the largest sources of funding and knowledge for developing countries, we help solve the world’s greatest development challenges. We work with public and private sector partners, invest in groundbreaking projects, and use data, research, and technology to bring tangible and transformative change around the globe. For more information, visit www.worldbank.org. VPU Context: The World Bank Group's Digital & AI Vice Presidency (D&AI VPU) leads the institution's work on digital transformation and artificial intelligence — both as a driver of development outcomes in client countries and as a foundation for institutional effectiveness. As AI systems become increasingly embedded in public services, financial systems, infrastructure planning, and humanitarian response, the imperative to ensure those systems are safe, fair, accountable, and aligned with human rights has never been greater. For more information: https://www.worldbank.org/en/topic/digitaldevelopment.
Unit Context: Trustworthy AI — encompassing principles of transparency, explainability, fairness, robustness, privacy, and accountability — is now a strategic priority for the World Bank Group in its advisory and operational engagements with client countries. At the same time, the security of AI-enabled systems is inseparable from their trustworthiness. Cybersecurity risks, including adversarial attacks, model poisoning, data breaches, and system vulnerabilities, pose direct threats to the integrity and reliability of AI deployments in development contexts. To advance this agenda, the D&AI VPU is seeking a Senior Digital Specialist with deep expertise in trustworthy AI and a solid foundation in cybersecurity. The specialist will lead and contribute to analytical work, operational support, policy dialogue, and knowledge generation across the Bank's global portfolio. Duties and accountabilities: Objectives: The overarching objective of this position is to strengthen the World Bank Group's capacity to design, deploy, govern, and evaluate AI systems in a manner that is trustworthy, secure, and aligned with the development goals of client countries. The specialist will play a leading role in mainstreaming trustworthy AI principles into the Bank's operations and supporting clients in building the regulatory, institutional, and technical capacity required to govern and deploy AI responsibly.
- Technical and Analytical Leadership* Lead the development of frameworks, toolkits, methodologies, and standards for the assessment and implementation of trustworthy AI systems in development contexts, covering fairness, accountability, transparency, explainability, and robustness.
- Conduct and oversee technical assessments of AI systems deployed or under consideration in client country operations, identifying risks related to bias, opacity, data integrity, and cybersecurity vulnerabilities.
- Provide authoritative technical guidance on AI risk management, including threat modeling for AI systems, adversarial robustness testing, and the intersection of AI safety and cybersecurity.
- Contribute to cutting-edge research and knowledge products on topics such as algorithmic auditing, AI governance frameworks, secure AI deployment in low-resource environments, and emerging regulatory approaches.
- Operational Support and Project Implementation* Provide technical inputs into the preparation, appraisal, supervision, and evaluation of World Bank lending operations and technical assistance programs with AI components across sectors (health, education, social protection, finance, agriculture, public administration, etc.).
- Serve as a task team member or co-TTL on select projects with significant trustworthy AI and digital security dimensions, ensuring that technical standards and safeguards are embedded into project design and implementation arrangements.
- Support borrowing countries in developing national AI strategies, AI governance frameworks, and regulatory environments that promote innovation while mitigating risk.
- Advise on the procurement, piloting, and oversight of AI systems in the public sector, including the design of government accountability mechanisms and civil society engagement frameworks.
- Advise on private-sector AI solutions in the context of WBG investments or advisory operations, including AI acceleration programs.
- Cybersecurity Integration* Apply foundational knowledge of cybersecurity principles — including information security management, identity and access management, network security, secure software development lifecycles, and incident response — to the assessment and design of AI-enabled systems.
- Advise on cybersecurity risks specific to AI deployments, including data poisoning, model inversion attacks, adversarial examples, and the vulnerabilities introduced by third-party AI providers and cloud infrastructure.
- Collaborate with the WBG's cybersecurity teams and relevant global partners to ensure AI systems in client operations meet internationally recognized security standards (e.g., NIST AI RMF, ISO/IEC 42001, OWASP AI Security and Privacy Guide).
- Support clients in developing institutional capacity for AI-specific cybersecurity governance, including the integration of AI risk into national cybersecurity strategies.
- Policy Dialogue and Client Engagement* Engage in high-level policy dialogue with government counterparts, regulatory authorities, civil society, and the private sector on the governance of AI and digital technologies.
- Represent the World Bank Group in relevant international forums, working groups, and multi-stakeholder platforms focused on AI ethics, trustworthy AI standards, and digital governance (e.g., GPAI, OECD, ITU, G20 Digital Economy Working Groups).
- Provide strategic advice to client governments on the design and implementation of AI regulatory frameworks, ethics guidelines, and institutional oversight mechanisms.
- Facilitate dialogue between technical, legal, and policy stakeholders to bridge gaps between AI governance theory and implementation practice.
- Knowledge Management and Capacity Building* Lead the development and dissemination of knowledge products including policy notes, working papers, operational guidance notes, e-learning modules, and toolkits on trustworthy AI and AI cybersecurity.
- Design and deliver capacity-building programs, workshops, and training sessions for client country officials, World Bank task teams, and development practitioners.
- Contribute to the D&AI VPU's global communities of practice and knowledge platforms, fostering peer learning and cross-regional exchange on AI governance.
- Mentor junior staff and consultants within the unit, contributing to team learning and professional development.
- Partnerships and Resource Mobilization* Identify and cultivate strategic partnerships with leading academic institutions, standards bodies, civil society organizations, and bilateral and multilateral partners working on trustworthy AI.
- Contribute to the development of concept notes and proposals for trust-funded and co-financed programs related to AI governance and digital security.
- Engage with the private sector, including AI developers and platform providers, to promote responsible AI practices and build awareness of development context needs.
Selection Criteria
Education:* Advanced degree (Master's or Ph.D.) in Computer Science, Artificial Intelligence, Information Security, Data Science, Public Policy, or a closely related discipline. A combination of technical and policy expertise is highly desirable.
Experience:* A minimum of eight (8) years of relevant professional experience in AI, machine learning, or digital technology, with demonstrated specialization in at least three of the following areas: AI ethics and fairness, explainable AI, AI risk management, AI governance and regulation, algorithmic auditing, or responsible AI deployment.
- Demonstrated experience applying cybersecurity principles in the context of digital systems, with at least foundational working knowledge of cybersecurity frameworks (e.g., NIST Cybersecurity Framework, ISO 27001) and an understanding of how security risks intersect with AI systems.
- Experience working in or advising government institutions, international organizations, or development finance institutions on AI or digital policy is strongly preferred.
- Experience in low- and middle-income country contexts, including work in fragile or resource-constrained environments, is an asset.
- Track record of producing high-quality analytical work, knowledge products, or policy guidance on trustworthy AI or related topics.
Technical Competencies:* Deep technical knowledge of machine learning algorithms, AI model development pipelines, and common failure modes of AI systems.
- Proficiency in AI fairness metrics, bias detection techniques, and explainability methods (e.g., SHAP, LIME, attention mechanisms).
- Familiarity with international AI governance frameworks and standards, including OECD AI Principles, EU AI Act, NIST AI Risk Management Framework, and ISO/IEC 42001.
- Working knowledge of cybersecurity concepts relevant to AI, including adversarial machine learning, data governance and privacy (e.g., GDPR, differential privacy), secure model deployment, and cloud security fundamentals.
- Ability to assess and design AI governance structures including algorithmic impact assessments, audit processes, human oversight mechanisms, and redress systems.
General Work Competencies:* Strong analytical and problem-solving skills, with the ability to navigate complex technical and policy challenges.
- Excellent written and oral communication skills in English, with the ability to translate technical concepts for non-specialist audiences, including senior government officials and policymakers.
- Demonstrated ability to work in a multicultural, multidisciplinary team environment and to build consensus across diverse stakeholders.
- High level of integrity, discretion, and professional judgment.
- Proficiency in an additional World Bank working language (French, Spanish, Arabic, Portuguese, Russian, or Chinese) is an advantage.
As the World Bank Group continues to evolve, we value candidates who:
- Demonstrate the ability to collaborate effectively across teams, disciplines, cultures, and institutional boundaries.
- Responsibly leverage digital and AI-enabled tools to enhance productivity, learning, and decision-making.
- Bring experience working in fragile, conflict-affected, and violent (FCV) contexts and are encouraged to highlight relevant experience in their application materials, where applicable.
WBG Culture Attributes:
- Sense of urgency: Anticipate and quickly respond to the needs of internal and external stakeholders.
- Thoughtful risk-taking: Challenge the status quo and push boundaries to achieve greater impact.
- Empowerment and accountability: Empower yourself and others to act and hold each other accountable for results.
World Bank Group Core Competencies
The World Bank Group offers comprehensive benefits, including a retirement plan; medical, life and disability insurance; and paid leave, including parental leave, as well as reasonable accommodations for individuals with disabilities.
We are proud to be an equal opportunity and inclusive employer with a dedicated and committed workforce, and do not discriminate based on gender, gender identity, religion, race, ethnicity, sexual orientation, or disability.
Learn more about working at the World Bank and IFC including our values and inspiring stories.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At World Bank Group, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
World Bank Group AI Hiring
World Bank Group has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Washington, DC, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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