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About This Role
About KIND
Kids in Need of Defense (KIND) is a global leader in the protection of unaccompanied and separated children who are forcibly displaced from their countries of origin. Launched in 2008, KIND champions a world in which every child’s rights and well\-being are protected throughout their journey to safety.
Building on its extensive programming and successful model in the United States, KIND is expanding in the Americas and Europe to bring its experience to bear in working with unaccompanied and separated children. Our work focuses on addressing the legal and mental health needs of this population; building strong partnerships and engaging in training and capacity strengthening with governments, NGOs, and the private sector; and advocating for systemic and pragmatic reforms to advance the rights of unaccompanied and separated children. Position Summary
KIND seeks a Senior Advisor, Global Fundraising to serve as the development team’s lead strategist and collaborator for international revenue development. Reporting to the Vice President, Development, this incumbent builds and manages a diversified portfolio of institutional, multilateral, governmental, and philanthropic funding relationships—positioning KIND as a credible global partner in the protection of unaccompanied and separated migrant and refugee children across a number of key regions, including Europe and Latin America.
This role emerged from KIND’s consulting engagement on European philanthropic landscape analysis, which revealed that rapidly shifting global funding dynamics—joint fundraising models, multilateral partnerships, and cross\-regional opportunity—require a senior, full\-time internal leader with the expertise and relationships to develop and execute a global development strategy at scale. The Senior Advisor will work at the intersection of development, program design, and international partnerships—partnering closely with Programs, Policy \& Advocacy, Communications, Finance, and Legal to ensure that global opportunities are pursued coherently and that fundraising strategy informs—and is informed by—programmatic decision\-making. A particular priority is co\-leading the KIND\-UNHCR Deployment Scheme, which would embed KIND child protection experts within national government systems across an initial set of pilot countries.
This is a remote position based from any location in the U.S.
This position is contingent upon continued funding.
### Essential Functions
Global Fundraising Strategy \& Leadership
- Develops and implements a comprehensive global fundraising strategy aligned with KIND’s mission and growth objectives, with an initial focus on Europe, Latin America, and UNHCR priority regions.
- Establishes revenue goals, prospecting frameworks, and portfolio planning across institutional, governmental, multilateral, philanthropic, and private funding streams.
- Centralizes and coordinates all non\-U.S. funding activity, eliminating duplication, establishing cohesive messaging, and maintaining consistent moves management across departments.
- Conducts internal audits of global fundraising capacity to inform staffing decisions, country selection, and multi\-year investment planning.
Donor Identification, Cultivation \& Portfolio Management
- Identifies and cultivates a diversified donor pipeline spanning European and Latin American donor states, development banks (IDB, World Bank, CAF), pooled funding mechanisms, EU instruments, private foundations, and corporate partners.
- Aligns donor outreach with international funding cycles, including UNHCR priorities, end\-of\-year donor budget decisions, and time\-sensitive windows such as OCHA\-Americas obligations.
- Implements rigorous moves management protocols—ensuring continuity, clear engagement pathways, and strategic deployment of senior leadership relationships.
- Advances KIND’s transition from single\-donor pilots to a multi\-donor, multi\-country funding platform.
Funding Landscape Analysis \& Opportunity Assessment
- Monitors global and regional funding trends across KIND’s priority geographies, including Latin America (Colombia, Ecuador, Honduras, Guatemala), Europe (Spain, Canary Islands, Brussels), Africa, Asia, and MENA.
- Assesses KIND’s readiness for fundraising in new geographies, including legal and procedural requirements, infrastructure needs, and operational capacity.
- Identifies and develops scalable program models—such as return and reintegration, legal\-psychosocial, and advocacy frameworks—as donor\-ready funding vehicles across regions.
- Produces strategic briefs and analyses to inform leadership and Board decisions on global fundraising opportunities.
Positioning \& Value Proposition
- Develops and adapts KIND’s value proposition for international audiences—tailoring messaging across funding channels, cultural contexts, and donor types.
- Positions KIND as a value\-added co\-fundraising partner to UNHCR and other multilateral institutions, leveraging KIND’s evidence base, technical credibility, and private sector engagement capacity.
- Ensures KIND’s global strategy considers organizational risk management and revenue diversification—complementary to, not in competition with, the domestic mission.
Internal Coordination \& Cross\-Functional Alignment
- Represents Development in cross\-functional global strategy discussions, ensuring that program design, country selection, and partnership decisions are integrated with fundraising strategy.
- Establishes coordination structures—clear role definitions, decision\-making authority, and regular cadences—across Development, Programs, Partnerships, Advocacy, Communications, Finance, and Legal.
- Consolidates and coordinates international donor relationships currently managed across multiple teams, ensuring strategic and consistent engagement.
- Prepares senior leadership for high\-level donor and partner engagements, including briefings, talking points, and relationship context.
Platform Scale\-Up \& Long\-Term Sustainability
- Transitions the KIND\-UNHCR Deployment Scheme from pilot to a multi\-year, multi\-country flagship platform, building a scalable co\-fundraising model.
- Coordinates negotiation of trilateral funding agreements between KIND, UNHCR, and pilot governments, with a view toward 2027 scale\-up.
- Connects funding opportunities across geographies—identifying synergies, sequencing investments, and building long\-term revenue sustainability.
- Informs decisions on global staffing, systems, legal registration, and operational infrastructure needed to support KIND’s expanding international footprint.
### Qualifications and Requirements
- Minimum of 15 years of progressive leadership experience in international development, humanitarian assistance, human rights, child protection, or a related global nonprofit field.
- Demonstrated success leading large\-scale international fundraising efforts, including joint fundraising and diversified portfolios spanning government donors (e.g., U.S. Government, European donor states), multilateral agencies (e.g., UNHCR and other UN bodies), foundations, pooled funds, and institutional philanthropy.
- Proven ability to operate at a senior strategic advisory level, supporting executive leadership on global revenue strategy, organizational positioning, and long\-term funding sustainability.
- Deep familiarity with international funding ecosystems, including donor compliance and procurement processes, multilateral partnership models, joint fundraising mechanisms, and cross\-border funding structures.
- Track record of building and managing high\-level external relationships with governments, UN agencies, foundations, and civil society partners, serving as a credible and trusted organizational representative.
- Demonstrated ability to work cross\-functionally in complex, matrixed organizations, partnering effectively with Programs, Partnerships, Advocacy, Communications, Finance, and Legal.
- Strong strategic planning and analytical capabilities, including assessing global funding landscapes, evaluating organizational readiness, identifying growth opportunities, and mitigating financial and operational risk.
- Experience supporting or scaling multi\-country, multi\-donor initiatives, including pilots transitioning to flagship platforms or long\-term global programs.
- Excellent written and verbal communication skills, with the ability to translate programmatic expertise into compelling donor narratives, value propositions, and funding strategies.
- Advanced degree in law, international relations, public policy, international development, or a related field strongly preferred.
- Ability to work remotely across time zones; international travel required.
- Working knowledge of Microsoft Office Suite (such as Teams, Excel, etc.).
- Ability to work collaboratively and multi\-task in our KIND environment, managing numerous priorities and emerging opportunities.
- Excellent organizational skills with the ability to work on multiple projects in a deadline\-oriented environment; ability to prioritize tasks and delegate as appropriate.
- Ability to multitask and work with a sense of urgency in a dynamic, fast\-paced environment.
- Committed to practicing and supporting wellbeing and a work\-home life balance.
- Experience working and communicating in a remote environment preferred but not required.
Our Benefits
- Medical, dental, and vision insurance with KIND paying 100% of the employee only portion of the premium for one of the three medical plan options, dental, and vision.
- Pre\-tax flexible spending account (FSA) for both medical and dependent care.
- Pre\-tax transit and parking spending account.
- Employer\-paid life insurance and accidental death and dismemberment insurance.
- Employer\-paid short and long\-term disability insurance.
For a complete list of benefits, please click here.
Our Focus on Wellness
KIND recognizes that our ability to help our clients starts with helping our team members. KIND has prioritized wellness for employees through Mindfulness and Wellness Trainings, Wellness Platforms, Employee Assistance and Resilience Programs, Time Away and Office Wellness Activities.
For more information regarding our Wellness initiatives please visit this link.
Application Instructions
To be considered for this role, please submit an employment application at supportkind.org/join\-the\-team, along with your resume and cover letter.*Disclaimer:* *KIND is committed to an ethical recruitment and hiring process and maintains a firm “no fees” recruitment policy. We will never charge a fee or ask for money as part of the application process. KIND also conducts all interviews via telephone or video conference, and at no time will KIND engage in a text or mobile app\-based application or interview process. For more information, please visit the following website:* *https://supportkind.org/join\-the\-team/kind\-employment\-practices/**.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $128K-$160K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Kids in Need of Defense, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($144K) sits 13% below the category median. Disclosed range: $128K to $160K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Kids in Need of Defense AI Hiring
Kids in Need of Defense has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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