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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
We are seeking a high-ownership and rigorous Chief Financial Officer to help architect the institutional backbone of a rapidly scaling AI-focused global development organization. This founding role will partner closely with the CEO and leadership team on financial strategy, organizational scaling, and AI-first operational infrastructure. While we are currently incubated at Evidence Action, 2AI plans to spin out on November 1, 2026 as an independent organization.
The right person thrives at the intersection of institutional rigor and startup pragmatism. You will set the strategy, own the outcomes, and serve as the internal expert, while identifying, contracting, and managing high-quality external partners: accountants, attorneys, payroll providers, auditors, insurance brokers, and state compliance vendors. You are comfortable in a high-ownership, high-ambiguity role, understand how AI can be used to improve the finance & accounting process, and excited by the opportunity to define an organization’s operating system from scratch.
You Will:
Strategic Leadership
- Design and stand up 2AI's core financial architecture: accounting systems, budgeting, forecasting, cash management, internal controls, and a reliable month-end close, sized for an organization that will grow several times over.
- Create an AI-native set of accounting & finance processes: Ensure that you are using AI to drive meaningful efficiencies across 2AI’s financial architecture.
- Run analyses to drive strategic organizational decisions: Build financial models, scenario plans, and organizational forecasts to support strategic decision-making in a fast-scaling environment.
- Design, procure, and oversee a lean, high-performing finance and compliance ecosystem: Manage outsourced partners (e.g., accounting, audit, legal, payroll, insurance, and HR) while ensuring strong institutional controls without unnecessary bureaucracy.
- Own the annual audit, Form 990, and statutory and grant reporting obligations, holding clean institutional controls from the first close onward.
Drive Organizational Expansion and Compliance
- Lead the financial, legal, and compliance workstreams that establish 2AI as an independent 501(c)(3): entity creation, registrations, banking, payroll, benefits, insurance, and staff transfers.
- Enable our expansion: Design financial systems capable of supporting rapid international scaling and increasingly complex funding structures.
- Drive high-leverage resource allocation: Partner with leadership to build a culture of analytically rigorous budget allocation and organizational decision-making.
- Build and own compliance infrastructure: Own critical compliance and institutional risk infrastructure, including charitable registration, insurance, governance, and regulatory filings.
- Drive governance and board operations: Establish board meeting cadence, conflict-of-interest disclosures, and fiduciary orientation for new members.
Partner on Grants, Operations, and Legal
- Oversee the entire grant lifecycle: build funder-facing budgets and models, lead financial diligence with major funders, and own compliance and spend-down against restricted grants.
- Manage legal affairs & contracting: Serve as the primary internal point of contact for all legal matters; review and negotiate vendor contracts, grant agreements, MOUs, and partnership arrangements, while ensuring IP protections are in place for open-source assets.
- Ensure compliance, manage risk, and ensure the organization's adherence to legal and regulatory requirements.
Requirements
The ideal candidate has built key organizational infrastructure in high-growth, complex settings (including systems, teams, and institutions) and is excited to do it again at an entirely new frontier. Ideal candidates are motivated by the prospect of building an organization that will reach 100s of millions of people, understand that we must act urgently to ensure AI’s benefits reach people in poverty globally, and bring the 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. 2AI 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:
- 12+ years in finance, including time as the most senior finance leader in an organization, business unit, or country program, with accountability for a full set of books.
- A track record as a builder: you have stood up or rebuilt a finance function rather than inherited a working one, and you can clearly separate what you created from what you were handed.
- Multi-entity, cross-border finance experience in LMIC settings: establishing and managing finance operations in other countries and navigating local statutory, tax, and foreign contribution requirements is a strong plus. Familiarity with managing legal and regulatory challenges in regions such as Africa, South Asia, and the US.
- Comfort in a lean model: you stay closer to the ledger than a CFO of a large organization would, and you select and manage outside providers (counsel, accountants, auditors, brokers, payroll) rather than hiring the capability in.
- Strong written communication skills; able to translate complex financial and legal concepts clearly for board members, program staff, and funders.
- Ability to assess complex data, foresee trends, and make strategic decisions to advance organizational goals.
- CPA, JD, MBA or equivalent professional credential strongly preferred; nonprofit-specific training or certification (e.g., CGAP, CNAP) a plus.
- Experience with nonprofit finance, compliance, and 501(c)(3) environments strongly preferred: e.g., Form 990, single audit, charitable solicitation registration, IRS rebuttable presumption, and state-level tax exemptions.
- Range across strategy and execution: you can pressure-test a multi-year scenario in the morning and fix the expense policy in the afternoon, with low ego about either.
- Curiosity and creativity with AI tools, including a testing mindset and eagerness to experiment with emerging technologies.
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 $240,000 - $270,000 USD per year.
While we are currently incubated at Evidence Action, 2AI plans to spin out on November 1, 2026 as an independent organization. We will offer a comprehensive benefits package, including health, vision, and dental, retirement plans, generous and flexible leave options, as well as other employee perks.
Salary Context
This $240K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. C-Level-level AI roles across all categories have a median of $250,000. This role's midpoint ($255K) sits 20% above the category median. Disclosed range: $240K to $270K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Evidence Action AI Hiring
Evidence Action has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span US, New Delhi, Delhi, India, New York, NY. Compensation range: $240K - $270K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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