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ABOUT TEAM4TECH
Team4Tech (www.team4tech.org (http://www.team4tech.org)) envisions a world where all learners have equitable access to quality education for improved socioeconomic mobility. We advance this mission by serving as an impact accelerator for community\-based education NGOs, providing them with technical, pedagogical and operational support to help them build the skills their learners need for future employability. Over the past 14 years, we have supported more than 1,400 NGOs in 110 countries, improving learning opportunities and developing durable skills for 60M under\-resourced learners.
Team4Tech's accelerator programming contributes to UN SDG 4 \- to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Using evidence\-based practices that promote education for employability, our accelerator programming, co\-designed with community\-based and education\-focused NGOs operating in low\-resource environments, includes:
- A robust, global online Community of Practice (CoP) to engage with free education and technology tools, workshops, certification courses, learning cohorts, and other resources;
- Facilitated regional hubs (in Africa, South Asia, Latin America \& the Caribbean, and the USA \& Canada) for localized knowledge sharing and collaboration;
- Access to skilled volunteers and educational resource partners to deliver pro bono capacity\-building projects; and
- Access to funding, amplification, and coaching to further promote NGO sustainability.
The rapid evolution of generative AI brings a new opportunity for personalized adaptive learning at scale for learners in under\-resourced contexts around the world. Team4Tech is building upon the technical and pedagogical foundations we have helped develop with NGOs globally to now develop their capacity to use AI to enhance their education programs. Over the past two years, we have become the leading AI accelerator for education NGOs in low income contexts by delivering 10 GenAI capacity building training cohorts for 80\+ NGOs in more than 20 countries.
WHAT YOU'LL DO
Team4Tech seeks an exceptional AI in Education Program Director to scope and manage highly impactful education technology projects and lead NGO training cohorts with defined targets, strategies, and goals. You will join a dedicated team of Program Directors focused on co\-designing solutions with global education NGOs and supporting their progress as they accelerate their impact in bridging the digital equity gap for learners in under\-resourced communities
Specifically, the Program Director will:
- Lead technical assistance projects and advisory work: Plan and run technical assistance projects with corporate and philanthropic partners, helping community\-based NGOs in low\-income countries put AI to practical use in their education work.
- Design and/or deploy AI readiness assessments: Create tools and frameworks to evaluate how ready NGO partners are to adopt AI, both technically and pedagogically.
- Design and run training cohorts: Lead training cohorts for NGO staff that help them implement AI to support better teaching and learning practices.
- Manage skilled volunteer projects: Scope NGO pro bono consulting projects, and manage volunteer selection, project matching, training, project delivery, and evaluation. Manage project implementation with corporate skilled volunteers across a range of virtual and in\-person project formats.
- Ensure quality and share what works: Make sure projects deliver strong results, and share capacity development approaches and lessons learned with the wider Team4Tech Community of Practice so others can apply and scale them.
- Coordinate with Program Directors and Regional Hub Leads: Partner within the team to promote proven practices and resources for employability\-focused education.
- Analyze program data: Work with other teams to analyze data and turn it into insights that feed into reporting, planning, and program design.
- Manage relationships: Build and maintain relationships with NGOs, corporate partners, and volunteers, and keep them updated on project progress and results.
WHO YOU ARE
A passionate advocate for Team4Tech's mission and values. You share Team4Tech's vision and passion to expand access to quality education and economic opportunity for under\-resourced learners around the world by leveraging technology, skilled volunteers and training resources. You thrive in an organization that believes in empowering everyone as a learner and everyone as a teacher.
AI Capacity Builder \& Strategist: You possess direct experience leading AI for education initiatives in low\-resource settings, AI training content development, or strategic AI advisory projects. You understand how to leverage Large Language Models (LLMs) and adaptive learning platforms to optimize pedagogical outcomes through evidence\-based teaching and learning practices. Your exposure to AI has equipped you to lead thoughtful collaborations with NGOs worldwide, expanding best practices in AI implementation.
A versatile and well\-rounded educator and project manager. You bring 5\+ years of applicable experience in educational technology program management, ideally leading technology strategy and implementation in an educational setting. You have an excellent understanding of project management techniques and methods, and you exhibit organizational, communication, and leadership skills.
A thoughtful relationship builder who develops rapport easily and fosters long\-term connections. You are an ambitious go\-getter, able to thrive in a fast\-paced environment. You have a warm, energetic, and authentic personality that translates to building lasting relationships both internally and externally.
A well\-organized and resourceful team player. You are experienced in managing multiple projects and tasks, proactively and independently addressing challenges as they arise. You have a mind for operations and efficiency, and strategically use limited resources to create systems and processes.
An inspired storyteller and communicator. You are skilled in analyzing, breaking down, and communicating complex ideas in ways that inspire others to action. You demonstrate an understanding of the context surrounding education and international development, and create logical arguments that clearly articulate the ‘why' behind mission, impact, and technology solutions.
A professional with international development experience. You have a track record of working in diverse cultural settings and are comfortable traveling internationally. Your exposure to international development contexts has equipped you with insights and sensitivities crucial for collaborating and communicating across cultures.
Preferred skills and experiences:
- Master's degree in International Development, Public Policy, Educational Technology, Global Data Governance, or a related field.
- Demonstrated success navigating multi\-stakeholder collaborations involving corporate social responsibility (CSR) entities, multilateral institutions, and/or international NGOs.
- Familiarity with managing and implementing skilled volunteering models.
- In\-depth knowledge in educational technology, with direct, practical experience utilizing AI\-powered tools in educational settings and under\-resourced contexts or supporting others to do so.
- Significant international experience, particularly in low\- and middle\-income countries with limited technology resources.
- Experience leading capacity building for NGO staff and/or teachers, ideally supporting NGOs/teachers to use AI to improve organizational/administrative efficiency and effectiveness and strengthen learner\-level outcomes.
- Ability to develop contextualized, evidence\-based training programs and resources for a global audience.
- Skilled in building relationships with key decision\-makers in the corporate social impact, education technology and global development sectors.
- Exceptional communication skills, both verbal and written.
- Ability to analyze and report on program data for strategic decision\-making and impact assessment.
- Spanish language ability is a plus.
LOCATION AND TRAVEL
Team4Tech is a remote\-first workplace. All roles are full\-time and remote, with the expectation that the employee is willing and able to travel periodically.
The candidate is expected to travel mostly internationally, approximately 20\-30% of the year, for various work\-related activities.
Unfortunately, we are unable to provide US sponsorship for employment at this time.
COMPENSATION
Salary is commensurate with experience and geographic location, ranging from $80,000\-$115,000 per year. Benefits include a health insurance stipend, an internet stipend, unlimited leave, and voluntary 401k contributions.
TO APPLY
Please submit your application through our Breezy HR portal. Only applications submitted through our Breezy HR portal will be considered at this time.
Applications will be considered on a rolling basis and handled with confidentiality. Preference will be given to applications received by July 17th. Desired start date is September 2026\.
Salary Context
This $80K-$115K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Team4Tech, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($97K) sits 55% below the category median. Disclosed range: $80K to $115K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Team4Tech AI Hiring
Team4Tech has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $115K - $115K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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