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About This Role
About The AI Access Initiative (2AI)
We're at an inflection point in artificial intelligence – presenting both tremendous potential opportunity and risk for people in developing countries. AI capabilities are improving on a timescale measured in months, while development systems often move on timescales measured in years and decades. Much of the technology needed for positive impact exists today, but few organizations exist to bridge research, deployment, and scaled implementation: we must act now to ensure AI’s benefits reach people in poverty in LMICs.
Incubated at Evidence Action, we’re scaling The AI Access Initiative, an organization focused on scaling AI\-enabled ‘big bets’ to benefit tens or hundreds of millions of people in poverty in low\- and middle\-income countries (LMICs). We operate at the intersection of global development actors, top AI labs, and leading researchers to drive meaningful access to AI’s benefits for the 3\.5 billion people living in poverty globally. We create “public good,” open\-sourced playbooks, toolkits, and insights that define how to design and launch tractable and impactful AI\-enabled programs. Given the scale of opportunity, we expect our portfolio to expand substantially, but to start, we’re scaling two programs focused on AI in Agriculture and AI in Health.
We are led by former Evidence Action CEO Kanika Bahl, a founding member of Anthropic’s Long\-Term Benefit Trust, and advised by Nobel Laureate Michael Kremer; Dario Amodei, CEO of Anthropic; and Kent Walker, President, Global Affairs for Alphabet and Google. This work builds on Evidence Action’s track record reaching 530M\+ people with cost\-effective, evidence\-based programs across 9 countries in Africa and Asia, with a focus on last\-mile delivery.
The Role
2AI is building fast. We have a founding team, two live program portfolios, government partnerships in India reaching millions of farmers, and a pipeline of opportunities that will drive significant expansion. The programs we are running have credible pathways to 100s of millions of people. While incubated at Evidence Action, we plan to spin out in Q4 ‘26\.
We are hiring two roles at this level: Vice President, Agriculture and Vice President, Health, each responsible for programmatic strategy and execution across their respective portfolio.
Agriculture: you will set programmatic strategy and drive rapid expansion for our Agriculture portfolio, working in close collaboration with our regional leaders, including our India Country Director, and building the program teams and partnerships required to drive population\-level scale. We have an ambitious goal to reach 100M\+ farmers with AI\-enabled weather forecasts, expand forecast R\&D to generate novel forecasts, and build public and private sector (WhatsApp, telcos) delivery channels to deliver new, LLM\-generated voice and image content that maximizes behavior change.
Health: Similarly, you will set programmatic strategy and drive learning, innovation, and scale in our Health portfolio, building new approaches and partnerships to drive population\-level scale. Here, our initial goal is to scale AI\-enabled clinical decision support to 20M\+ people via telemedicine platforms, dramatically improving diagnosis and health outcomes at a national level. We are also planning to explore and test novel interventions for low\-resource settings, including direct\-to\-consumer (DTC) health interventions.
In either role, you will also be a key face of 2AI to funders and partners for your portfolio. As a core member of the leadership team, you will have a meaningful hand in shaping the direction of where 2AI goes next.
These are builder roles: recruiting and developing program teams, designing organizational infrastructure that doesn't yet exist, pushing into new geographies and intervention areas. History shows that without a clear driver and coordinated action, complex innovations in the developing world risk languishing for decades. The cost of moving slowly here is measured in lives and livelihoods — hundreds of millions of people who could benefit from AI\-powered health and agricultural tools if the field acts with sufficient urgency and rigor. This role exists to prevent that delay.
You Will:
Build, Scale, and Learn
- Serve as a core member of the executive leadership team, shaping organizational strategy, culture, and enterprise\-wide priorities.
- Hire and mentor a high\-performing leadership team and staff, creating a highly mission\-driven culture focused on moving with the scale, thoughtfulness, and urgency required.
- Ensure every program has a strong learning architecture: clear hypotheses, rigorous measurement, rapid iteration cycles, and honest reporting. Use cost\-effectiveness evidence to guide resource allocation — scaling what earns it, cutting what doesn't.
- Develop and steward 2AI's open\-sourced playbooks and insights — building durable field knowledge, not just running programs.
Program Strategy \& Design
- Set and achieve ambitious program strategies for 2AI's Agriculture or Health portfolios — vision, priorities, delivery models, and expansion roadmap — in close collaboration with the CEO and regional and program leads. Achieve high\-caliber reach and impact for hundreds of millions of people.
- Design and run fast, rigorous pilots. This includes strong measurement and feedback loops from day one, rapid iteration on what isn't working, and clear\-eyed calls on what deserves to scale.
- Stay at the frontier of AI\-enabled solutions for LMICs; actively tracking, testing, and integrating emerging tools and approaches. Bring intellectual curiosity and the ability to learn rapidly in a fast\-moving space, translating what's technically possible into what's practically scalable for underserved populations.
- Partner with technical and research teams to ensure AI\-powered approaches are grounded in rigorous evidence, adapted to local realities, and designed for sustainable delivery.
Build Partnerships and Mobilize Resources
- Serve as 2AI's programmatic bridge among AI labs, global development actors, leading researchers, frontline implementers, and regional governments — translating credibly across all of these worlds.
- Shape technical offerings in partnership with leading AI labs, including adapting AI tools for underrepresented languages, low\-bandwidth environments, and last\-mile contexts.
- Support regional leaders — including our India Country Director — in navigating government relationships, adoption pathways, and policy enablement, providing strategic input and senior representation where needed.
- Lead efforts on fundraising — contributing to compelling narratives for major funders, leading grant development, and making the case for ambitious investment in the portfolio.
- Represent 2AI at senior levels in public and private forums, building 2AI's profile as the leading ecosystem coordinator for AI deployment in LMICs.
Requirements
The ideal candidate has built things — programs, teams, organizations — in complex, resource\-constrained environments, and is excited to do it again at an entirely new frontier. This is not a role for someone who wants to manage a steady\-state portfolio. It is for someone motivated by the prospect of programs that reach 100s of millions of people, who understands that the window to shape how AI lands in the developing world is open now, and who brings the urgency, rigor, and ambition that moment demands.
We are still actively scoping our leadership roles and structure, reflecting the dynamism of this fast\-growing phase of the organization — both the fun and the realities that come with rapid growth. Evidence Action is open to candidates across a range of seniority levels for this role; title, scope, and compensation will be assessed candidly with each candidate throughout the process based on their experience and fit.
You will likely have:
- 15\+ years of experience building and leading programs in international development, or adjacent fields, with a demonstrated track record of scaling complex initiatives to national or regional scale in low and middle income countries.
- A track record as a successful builder: you have built new programs and organizational functions in 3\-5 countries, scaled programs nationwide across these countries, recruited and developed top talent, and cultivated a culture of rigor and urgency.
- Experience with AI\-enabled solutions in your portfolio area at the program design or deployment level is preferred: AI clinical decision support, digital health, or telemedicine for the Health role; AI in agriculture, digital agriculture, or agtech for the Agriculture role.
- Experience in fundraising or major donor engagement — comfortable making the case to large HNW or institutional funders. You have raised $5M\+ annually.
- Familiarity with how African and/or Asian governments operate, with enough experience to support regional leaders in navigating institutional processes.
- Experience bridging technical and programmatic worlds in your portfolio area (global health delivery for Health; agricultural extension delivery for Agriculture), or more broadly in market\-shaping, technology for development, or related fields. Comfort translating between AI labs, researchers, delivery organizations, and government counterparts.
- Strong AI fluency: strong working knowledge of frontier AI tools and LLMs, and their practical application in low\-resource settings. Active curiosity about frontier development.
- Existing relationships with major global health or agriculture funders, including bilateral donors, large foundations, and tech sector philanthropists a plus.
Position Location
This role location is flexible anywhere within the United States for fully remote candidates. We are unable to sponsor or take over sponsorship of a U.S. employment visa at this time. Applicants must be legally authorized to work in the U.S. for roles based in the U.S.
Evidence Action is an Equal Opportunity Employer with a commitment to diversity. All individuals, regardless of personal characteristics, are encouraged to apply.
Benefits
The expected role range for this US position is listed below. We consider multiple factors when determining the base salary for a role, including but not limited to: role scope, program budgets, internal equity, and a candidate's qualifications and/or prior experience.
Note: Pay and benefits will be commensurate with the role specifications, local statutory requirements, and the cost of labor in the markets where we operate.
The pay range for this role is $210,000 \- $250,000 per year.
This role will initially be housed at Evidence Action, with the explicit plan to transfer with the AI Access Initiative as a founding member of the team when that project is spun out as a new entity later this year. At that point, benefits and policies may change. For US based roles, Evidence Action provides comprehensive benefits including international health care, HSA/FSA options, life insurance, disability coverage, retirement plans with a matching component, generous and flexible leave options, as well as other employee perks on a reimbursement basis. For more information visit our careers page or ask our recruiting team!
Salary Context
This $210K-$250K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Evidence Action, 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. This role's midpoint ($230K) sits 7% above the category median. Disclosed range: $210K to $250K.
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.
Evidence Action AI Hiring
Evidence Action has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $250K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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