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About This Role
### Position Summary
We are seeking a Chief Learning Officer for AI and Ed Tech to advance our global AI learning agenda, cultivate strategic partnerships in the AI and technology sectors, and coordinate efforts across the organization to leverage AI in our work. As a locally rooted and globally connected network operating across 60\+ countries, Teach For All is uniquely positioned to catalyze AI innovation towards educational transformation, and to share insights with the technology and education sectors.
As Chief Learning Officer (CLO) for AI and Ed Tech, this role will work with other Chief Learning Officers at Teach For All's Global Institute for Shaping a Better Future to lead a learning agenda exploring how to maximize AI's impact on education. The CLO will also lead a highly collaborative, cross\-functional "AI Circle" that brings together all staff at Teach For All working on AI adoption from different teams across the global organization—including core software/data engineers and learning specialists—to foster shared learning and collaboration across all aspects of our AI work. This role reports directly to Teach For All's CEO, Wendy Kopp.
### Key Responsibilities
1\. Advance the Global Institute's Global AI Learning Agenda (50%)
- Craft and Pursue a Cross\-Cutting Learning Agenda About AI and the Future of Education, working with the Global Institute's other CLOs driving our learning agendas around how to shift the purpose and outcomes of education; learning from lighthouse classrooms, learning environments, and systems; developing the people and collective leadership to enable progress; and measuring holistic outcomes. Foster inquiry, surface insights, and generate global frameworks on areas including the following:
+ *The Purpose and Future of Education in an AI\-Infused World:* *How should AI influence the purpose and intended outcomes of education that develops students holistically so they can shape a better future?*
+ *Transformational Teaching and Learning:* *How can AI be used responsibly, equitably, and effectively to support holistic learning for all students, and where might it be detrimental?*
+ *Educators as Co\-Architects:* *How can educators, especially those working in under\-resourced and marginalized contexts, be co\-architects in the development of AI tools to transform what those tools look like, and what do they need from AI developers and the wider field?*
+ *Developing Transformational Educators:* *How can AI support the development of teachers and leaders who grow all students holistically so that they can shape a better future?*
+ *Measuring Development:* *How can AI help us better understand and measure holistic student and educator development?*
2\. Drive Global Strategic Partnerships \& Ecosystem Cultivation (30%)
- Cultivate Partnerships with Foundational Model Owners: In close collaboration with our Global Development team, lead and manage multi\-layered strategic relationships with world\-leading AI organizations, including Anthropic, OpenAI, and Google, to ensure Teach For All is able to help shape the development of these foundational models' role in education.
- Maximize Real\-World Product Feedback Loops: Partner with tech industries to leverage our network's scale as a global testing ground, providing developer ecosystems with real\-world, mission\-aligned validation and product feedback from under\-resourced educational contexts.
- Secure Infrastructure and Resources: Broker institutional partnerships that unlock free or discounted software licenses, API access, hardware, and specialized engineering support to dismantle financial and technical barriers to innovation across our 60\+ network partners.
- Thought Leadership Globally: Elevate Teach For All's perspectives at international forums, global roundtables, and policy conventions, sharing evidence\-based frameworks to position network voices and insights at the center of global educational AI policymaking.
3\. Help Leverage AI to Advance Teach For All's Mission (20%)
- Lead the Cross\-Functional AI Circle: Convene the staff working on AI across Teach For All—those supporting network partners and global staff members with their own adoption, those building tools for Teach For All, those involved in brokering partnership with the AI companies—to foster cohesion in our work.
- Contribute to the Success of Teach For All's Global AI Strategy in areas such as:
+ *Scaling Teacher and Alumni Innovation:* Support the scaling and evolution of the AI Literacy \& Creator Collective—an 1,800\-member community of teachers, alumni, and staff across the Teach For All network who are collaborating and innovating in leveraging AI in education settings around the world. Foster more engagement across alumni AI innovators and partner program staff.
+ *Growing Network Partner Innovation and Impact:*In partnership with the Global Lead, AI for Programmatic Impact and the Head of AI Solutions \& Engineering, support partners' efforts to test AI\-enabled innovations that strengthen recruitment of fellowship participants, improve selection processes while centering inclusion and equity, and strengthen teacher training and leadership development.
+ *R**evolutionizing Cross\-border Learning:* Together with the Head of Technology and the Head of Platforms Strategy, consult on building an AI\-enabled learning network that breaks down barriers to knowledge and connection, so every network partner can access the right insights, in their language, at the moment they need it—with community ownership and human judgement at the center.
- Champion Ethical and Safeguarding Guardrails: Support the roll\-out and continuous iteration of the board\-approved AI\-use Policy and Guidelines, supporting critical cross\-functional conversations around child safeguarding, data privacy, and ethical compliance across classrooms.
### Qualifications
- Seasoned Strategic Leadership: 12–15\+ years of professional experience, with at least 7\+ years in strategic roles leading technological transformations, educational technology initiatives, or other roles with heavy involvement with the technology sector.
- AI Domain Fluency: Robust understanding of contemporary AI architectures, large language models, enterprise API integrations, RAG\-based systems, and custom data infrastructure (such as middleware and unified data layers).
- Excellent Relationship Management: Proven track record of cultivating and navigating multi\-million dollar funding relationships or strategic partnerships with top\-tier technology and/or venture capital firms.
- Mission Alignment: Deep commitment to Teach For All's vision and mission, contextual knowledge of the educational and structural realities facing underserved, low\-resource communities, and a passionate belief in the power of localized, collective leadership to solve systemic educational inequities.
- Responsible AI Guardship: Commitment to grounding technological advancement in core human values: equity, child safety, human judgment, and culturally responsive, ethical practice.
- Multi\-Stakeholder Orchestration: Demonstrated ability to align cross\-functional teams without relying purely on rigid hierarchical authority.
- Exceptional Communication \& Influence: Strong storytelling, public speaking, and written communication skills, with an ability to articulate highly technical AI concepts clearly and compellingly to diverse audiences.
- Inclusive Leadership: Comfort leading within highly diverse, globally distributed, and 100% remote team environments, exhibiting strong cross\-cultural awareness.
What Teach For All Offers:
- Commitment to cultivating a culture in which all staff members feel they belong, are valued for their contributions, and have an impact on our organization's progress
- Professional and personal development experiences and ample opportunities to make a positive impact on the work of Teach For All and beyond
- Comprehensive benefits package designed for your well\-being and work\-life needs
- Generous time off and flexible work arrangements
- And much more!
Application Instructions
If this opportunity sounds like the next best step in your career, please submit your resume or curriculum vitae (CV) and a one\-page letter of motivation (in English) directly online. Your letter should summarize motivations for your application and how your skills and experience align with the terms of this opportunity. We look forward to learning about you and your passion for ensuring educational opportunity for all!
Applications are reviewed on a rolling basis. Therefore, candidates are encouraged to apply at their earliest convenience.
Work authorization
This position does not offer employment visa sponsorship, immigration assistance, or support for work permits or residency applications. To be considered, candidates must already possess valid work and residency authorization to work in the country of hire at the time of application.
Compensation
Salary for this position is competitive and dependent on country of hire and prior work experience and includes a comprehensive benefits package.
Travel and Hours
Travel around 20% of the time may be required. Flexibility to work across different time zones is also expected, including occasional nontraditional hours for early or late calls.
About Teach For All
Teach For All is a global network of over 60 independent, locally led organizations and a global organization united by a commitment to developing collective leadership to ensure all children can fulfill their potential. Each network partner recruits and develops promising leaders to teach in their nations' under\-resourced schools and communities and, with this foundation, to work with others, inside and outside of education, towards a world where all children have the education, support, and opportunity to shape a better future. Teach For All's global organization works to increase the network's impact by supporting the development of new organizations; fostering network connectivity and learning; providing coaching and consulting; and enabling access to global resources for the benefit of the network.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Teach For All, 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 $214,900 based on 6,420 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Teach For All AI Hiring
Teach For All has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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