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AI EDUCATION & GLOBAL IMPACT INTERNSHIP
Women’s Entrepreneurship Day Organization (WEDO) / #ChooseWOMEN New York City / Hybrid & Remote Opportunities
Help us use AI to create opportunity for women and girls around the world.
Women’s Entrepreneurship Day Organization (WEDO) is a global nonprofit movement dedicated to empowering women and girls economically and helping them gain the education, connections, technology and opportunities they need to thrive.
Our initiatives have reached women and communities across 144 countries, working with universities, entrepreneurs, business leaders, investors, governments and global partners.
A major focus of our work is our AI Entrepreneurship Education Program, created to give underserved women and girls practical access to AI and entrepreneurship education. Our long-term goal is to help educate one million women and girls and ensure they are not left behind as AI transforms the global economy.
THE INTERNSHIP
We are looking for motivated, curious and mission-driven students who want hands-on experience at the intersection of:
Artificial Intelligence + Entrepreneurship + Education + Global Impact
This is not about sitting on the sidelines. Interns will learn how a global nonprofit develops, expands and measures an international education initiative while working alongside our team on real projects.
Depending on interests, experience and educational goals, interns may participate in projects including:
- AI Education: Research emerging AI tools and help identify practical ways entrepreneurs can use AI for research, marketing, operations, finance, sales, productivity and business development.
- Program Development: Help review and strengthen educational materials, resources, presentations and learning experiences for our AI Entrepreneurship Education Program.
- Global Expansion: Research universities, nonprofits, women's organizations, community groups and educational partners that could help bring the program to additional communities.
- Partnership Research: Learn how nonprofit partnerships are developed by researching potential educational, corporate, foundation and community collaborators.
- Impact & Data: Help organize program participation information, research impact-measurement approaches and explore ways WEDO can better understand and communicate program outcomes.
- Community Engagement: Support outreach and engagement with WEDO's international network of ambassadors, educators, entrepreneurs and community partners.
- Storytelling & Communications: Help identify powerful stories of women using entrepreneurship, technology and AI to transform their lives and communities.
- AI Innovation: Test new AI tools and propose creative ways WEDO can use technology to expand access to education and increase its global impact.
WHAT YOU WILL LEARN
Interns will gain exposure to how a global nonprofit initiative moves from an idea to implementation, including program development, AI applications, international partnerships, research, communications, impact measurement and nonprofit leadership.
You will also have opportunities to learn about the broader WEDO ecosystem and our work with successful women entrepreneurs, founders, investors, family offices, business leaders, universities and global changemakers.
WHO WE ARE LOOKING FOR
We welcome students from a wide variety of academic backgrounds. You do not need to be an AI or computer science major.
We are especially interested in students studying or interested in entrepreneurship, business, AI/technology, communications, marketing, international affairs, education, nonprofit management, social impact, data/analytics or women's economic empowerment.
Ideal candidates are:
- Curious and excited to learn
- Interested in AI and its potential for social and economic impact
- Strong researchers and communicators
- Organized and dependable
- Comfortable taking initiative while receiving supervision and feedback
- Passionate about creating opportunities for women and girls
- Excited to work with people and communities around the world
INTERNSHIP STRUCTURE
The internship is designed as a structured educational and professional-development experience. Interns receive supervision, guidance and feedback, and projects can be tailored where appropriate to a student's academic interests and learning objectives.
The internship is for a defined term and is intended to complement, rather than replace, the work of employees. Participation does not guarantee or create an entitlement to future employment with WEDO.
Academic credit is available where permitted by the student's college or university. WEDO is happy to work with eligible students and their schools on reasonable documentation required for internship or academic credit.
IMPORTANT INFORMATION
This opportunity is intended as an educational internship/volunteer experience with a nonprofit organization. Any unpaid placement will be structured in accordance with applicable federal and New York State requirements. Candidates will receive written information regarding the nature of the internship, compensation status, anticipated schedule, learning objectives and duration before beginning the program.
WEDO is committed to providing equal opportunities and welcomes qualified applicants without regard to race, color, religion, sex, national origin, age, disability, genetic information, or any other status protected by applicable law. Reasonable accommodations are available for qualified individuals with disabilities.
READY TO USE AI TO MAKE A DIFFERENCE?
If you want to learn how AI, entrepreneurship and global partnerships can create meaningful opportunity for women and girls, we would love to hear from you.
Apply with your résumé and a short note telling us:
- Why WEDO's mission interests you
- What you hope to learn during the internship
- Your area of study and current school
- Your availability and preferred internship period
- Whether you are seeking academic credit
Pay: $1,000.00 per year
Work Location: In person
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Women's Entrepreneurship Day Organization, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Women's Entrepreneurship Day Organization AI Hiring
Women's Entrepreneurship Day Organization has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% above the national 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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