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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 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 low- and middle-income countries (LMICs).
Incubated at Evidence Action, we're rapidly 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 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. 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 across Africa and Asia.
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 The AI Access Initiative is at an early and critical juncture. We’re building a high-performing, globally distributed team from the ground up. We are hiring a Chief People Officer to define and scale people operations for our team. This includes building lean systems to hire, onboard, and retain top talent to unlock meaningful upside for people in poverty globally. While we are currently incubated at Evidence Action, we plan to spin out on November 1, 2026 as an independent organization. New employees will be hired directly by 2AI.
This role sits at the intersection of talent, performance, and organizational design, leveraging AI to ensure thoughtful design of efficient systems. The CPO will lead the global talent and human resource function, have responsibilities to support the regional and country human resource teams, and report directly to the CEO, acting as a strategic thought partner and tactical operator on people decisions. You care deeply about the mission. You want to support the humans on this team including their growth, wellbeing, and ability to focus on work that matters, while building the AI-native systems and culture that make achieving our incredibly ambitious mission possible. You are a fast-moving but thoughtful builder, and excited to drive the engine behind the growth of a fast-scaling organization - growing by 40+ people in the coming year alone. And you are committed to creating a team of “A” players, including investing heavily in recruitment to attract top talent, and creating a high-performance, high-accountability culture that can move at the speed of AI.
You Will:
Design and execute the strategy for the People function:
- Set the vision and build out the strategy for the talent structure and function: drive effective and high-quality execution of the strategy; and ensure that an integrated people function (inclusive of acquisition, retention/development, and HR administration) is aligned to our organizational values and priorities.
- Create an AI-native set of people systems and processes: Ensure that you are using AI to drive meaningful efficiencies across recruitment, onboarding, hiring, and performance management, while helping create an AI-native culture at 2AI.
Build the hiring engine
- Own recruiting end to end: Run every stage of the hiring process in partnership with program teams, including sourcing, pipeline development, structured assessment design, offer construction, and close.
- Build pipelines before we need them: For senior and specialist roles, the best candidates aren't applying, they're being found. Develop sourcing strategies and maintain warm networks in markets where top talent is scarce.
- Design structured hiring processes with rigorous assessments: Ensure every role has identified key criteria, consistent evaluation, and interview panels that systematically test for core criteria and cultural fit.
- Assess AI engagement in candidates: Design creative processes to evaluate how candidates actually use AI tools, and maintain a current picture of frontier fluency in tools and capabilities.
- Build hiring capacity across the organization: Coach hiring managers to be exceptional interviewers, give calibrated feedback, and make decisions efficiently. The quality of our hires is one of the most important drivers of organizational success.
Design the organization
- Design structure ahead of growth: Partner with leadership on role design, team boundaries, reporting structures, and the sequencing of hires.
- Build and maintain high talent density: Create a culture where excellence is recognized and areas of growth are rapidly addressed. Know who is performing at or above role expectations, who is developing, and who is at risk.
- Navigate performance issues with good judgment: Coach leaders to move quickly from diagnosis to clear feedback to decisive action, with accountability to the mission and care for the individual. Encourage leaders to make compassionate but timely exits where needed.
- Connect talent insights to organizational planning: Translate the talent picture into concrete decisions on headcount, development investment, and how we structure roles.
Build people systems that scale
- Own the cultural formation in a global context: Has an understanding of and experience in 1) building and supporting cross-cultural teams and 2) maintaining a shared, global culture. Develops this across the people process from designing recruitment processes that select for values alignment, to shaping the first 90 days.
- Design onboarding that sets people up for success: Build a structured experience that orients new hires to the mission, their role, our values, the team, and what great performance looks like.
- Design a performance system people find genuinely useful: Create a process for honest conversations, meaningful development, and clear expectations.
- Build compensation frameworks that are equitable and defensible across geographies: We operate across LMIC and high-income country contexts. Develop a philosophy and structure that is fair, transparent, grounded in data, and explainable to staff.
- Use data and AI to drive people decisions: Build and maintain the tools and dashboards that give leadership a clear picture of headcount, attrition, hiring velocity, compensation equity, and team health.
- Stand up the people infrastructure for an independent entity: Source and manage a professional employer organization (PEO) or employer of record (EOR), and build the employment contracts, payroll, benefits, and policies required to operate post-spinout.
- Ensure compliance and safeguarding across operating jurisdictions: Understand the labor law landscape in our operating countries to keep us compliant. Establish the code of conduct, reporting channels, and investigation protocols we need to operate responsibly across our program countries and to meet donor standards.
- Own workforce planning and the people budget: Partner with the CFO to build headcount plans and cost models that reflect real hiring timelines and sequencing, and hold the plan as roles are added, changed, or deferred.
Strategic thought partnership
- Be brought in early on people decisions: Build credibility with leaders so they consult you when they're thinking through a hire, a team structure, or a difficult personnel situation, not once they've already decided.
- Push back when something doesn't feel right: If a hire doesn't align with what the structured process is telling us, say so. If a promotion is premature or a restructure creates unsurfaced risk, name it.
- Handle sensitive employee situations with discretion and judgment: Know when to escalate, when to resolve directly, and when to bring in external expertise. Protect the individual and the organization simultaneously.
Requirements
This is not a role for someone who wants to steward a mature HR department with established processes and a full team. It is for someone who is comfortable standing up hiring, performance, and compensation systems from scratch. Our ability to act at that speed depends on getting the right people into the right seats, repeatedly.
We are still actively scoping our leadership roles and structure, reflecting the dynamism of this fast-growing phase of the organization. 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:
- 12+ years in human resources, people operations, or talent roles, including senior leadership of a people function, with meaningful time spent in organizations that required you to build rather than inherit.
- A track record as a builder: you have designed hiring, performance, or compensation systems from scratch, and can clearly separate what you created from what you inherited.
- Demonstrated experience in full-cycle recruiting, including sourcing and closing senior hires in competitive or thin-talent markets.
- Experience working in or across LMIC contexts. You understand distributed workforce dynamics, labor law variability, compensation equity across geographies, and the cultural nuances of feedback and performance conversations.
- A track record of credibility with senior leaders. You've been brought in early, you've pushed back when it mattered, and you've been trusted with sensitive situations.
- Strong analytical instincts; you build tools and analyze data yourself rather than waiting for a dedicated analyst.
- Curiosity and creativity with AI tools, including a testing mindset and eagerness to experiment with emerging technologies. You can assess AI fluency in candidates because you practice it yourself.
- Comfort with ambiguity and fast iteration. Balances high EQ, rigor, and strong throughput with the levity and humility to tackle hard problems in a diverse, collaborative team. Ability to manage multiple competing deadlines, dynamically reprioritize as evolving program needs and resources require, embrace and structure ambiguity, and drive tasks to completion in a fast-paced environment
- Experience standing up employment infrastructure for a new or spinning-out entity is a strong 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: 220,000 - 240,000 USD per year.
While we are currently incubated at Evidence Action, 2AI plans to spin out on November 1, 2026 as an independent organization. New employees will be hired directly by 2AI. 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 $220K-$240K 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 ($230K) sits 8% above the category median. Disclosed range: $220K to $240K.
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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