Interested in this AI/ML Engineer role at Robin Hood Foundation?
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About Robin Hood
Founded in 1988, Robin Hood is a 501(c)(3) public charity that finds, fuels, and creates the most impactful and scalable solutions lifting New Yorkers permanently out of poverty in New York City. We convene philanthropists, civil society organizations, government, businesses, nonprofits, community members, and those living in poverty to develop and advance programs and policies that can make the American Dream real here in New York City.
Our investments include best-in-class organizations providing legal services, housing, meals, workforce development training, childcare, education, and other vital services that give New Yorkers the tools to build better lives. The programs it funds are often the first of their kind and serve as demonstration models to identify what works before being replicated in other cities. Because our Board of Directors covers 100% of all administrative, fundraising, and evaluation costs, every dollar donated goes directly to organizations helping New Yorkers in need.
While Robin Hood's primary focus is on New York, its impact extends far beyond the borders of the city. Its pioneering, metrics-based approach has become the gold standard for charities around the nation as Robin Hood works to understand and evaluate the impact of its partners to ensure that its philanthropic dollars are utilized to maximum impact. Robin Hood also supports its community partners and their leaders with financial and strategic support and management assistance that allows those organizations to maximize their impact.
Robin Hood also understands that if we are to support families living in poverty on the path towards financial independence, we must become an effective partner with government. We do this by collaborating with government partners to build program and policy innovations that hold the promise of expanding economic opportunity in New York City - providing nimble "risk capital" for high-potential strategies. We also work to advance public policies that research tells us expand economic opportunity. In this way, we seek to leverage our philanthropic investments by finding ways to advance the highest-impact strategies at scale.
Robin Hood has the unique ability to be a catalyzing force for those living in poverty, bringing distinct constituencies together to develop and spread the most effective strategies to advance economic mobility in New York City and beyond.
Our Approach:
• Focusing on Results: We invest in best-in-class organizations operating effective programs. We fund research to understand how poverty persists and what strategies are effective in fighting it. And we track grant outcomes and apply cost/benefit analyses to ensure every dollar we invest demonstrably improves the lives of low-income New Yorkers.
• Building Institutions that Transform New York City: We offer management advice, strategic real estate consulting, board placement services, financial and communications coaching, leadership development, and other technical assistance to help our partners increase scale and impact.
• Driving Innovation to Solve Unmet Needs: We invest in new leaders, new organizations, and new ideas that have groundbreaking potential.
• Scaling What Works: We partner with government and other funders to grow proven solutions in New York City and beyond, so more people benefit from investments that work.
Position Overview
The Managing Director, AI for Impact is a new position for the organization, established to drive Robin Hood’s “AI for impact” strategy. Applied thoughtfully, AI can accelerate new solutions and scale effective approaches across domains, including education, health, access to resources and supports, skills training, and more. At the same time, AI presents significant risks: without purposeful intention, it could deepen inequality, entrench disadvantage, and undermine upward mobility.
The Managing Director, AI for Impact will lead Robin Hood’s efforts to realize AI’s positive potential, with a clear understanding of its risks and a sustained focus on delivering meaningful impact in New York City and beyond. This is a role for someone with strong AI fluency and technology product experience who can identify big picture strategic opportunities and bring judgement about what makes technology projects succeed or fail.
Reporting to the Chief Program & Impact Officer, the Managing Director, AI for Impact is responsible for strengthening our AI-specific grantmaking, building the partnerships and relationships that help source funding opportunities, and expanding Robin Hood’s contributions to the field of AI and upward economic mobility. Internally, the role serves as a senior advisor on AI strategy, working closely with other organizational leaders, including the Chief Financial & Operations Officer, to ensure a consistent, principled approach in our AI adoption and application. The Managing Director, AI for Impact will directly oversee Blue Ridge Labs @ Robin Hood, an internal venture studio and funding vehicle for tech-enabled solutions that address poverty-related challenges.
Programmatically, Robin Hood has a well-established practice of funding technology-enabled solutions. This includes Blue Ridge Labs, which has a 10-year plus track record of incubating and accelerating successful, scalable digital-first organizations that address challenges of people experiencing poverty. Throughout its longer history, Robin Hood has consistently supported technology innovations, including leading edtech products and digital benefits access tools. More recently, Robin Hood co-created a discrete Learning + Technology Fund; hosted AI x Opportunity Summits that convene technology leaders from across sectors; ran a national AI Poverty Challenge; and, developed leading AI-related capacity building support for community partners.
Internally, Robin Hood has dedicated IT and Business Intelligence teams responsible for its organizational technical capacity and staff-facing tools. Robin Hood’s Partnerships team also engages closely with the technology community.
The Managing Director, AI for Impact will build on Robin Hood’s strong technology-related assets. The intent of the role is not to consolidate AI work under a single owner; the goal is to ensure the organization has a sharp, coherent, and forward-thinking approach across both our external
programmatic work and our internal AI-related efforts. The Managing Director, AI for Impact is not expected to hold domain expertise across all of Robin Hood’s areas; rather, the role brings technical sophistication, relationships, and credibility in the AI and technology community, and serves as a resource to Robin Hood’s program teams on AI-shaped problems and opportunities. Most AI-related grant decisions stay with those teams, who hold budget authority in their own domains — education, workforce, health, and others — and form a point of view on AI’s role in their fields. The exception is Blue Ridge Labs, which the Managing Director, AI for Impact directly oversees and which holds its own budget to fund early-stage, tech-based organizations.
Overall, the Managing Director, AI for Impact will work collaboratively with Robin Hood’s grantmaking teams, departments across the organization, and Robin Hood’s broader network to deepen our technology orientation, capabilities and poverty-fighting impact.
If you have spent your career where technology meets public purpose, this role offers something rare: the mandate, relationships, and resources to make that work matter for the New Yorkers who need it most.
Key Responsibilities
Key responsibilities include, but are not limited to:
Set and drive strategy
- Own Robin Hood's strategy for supporting AI that advances economic mobility - identifying where AI and digital tools can help more New Yorkers reach mobility milestones, and where AI disruptions risk causing harm.
- Engage across Program teams, Management Acceleration, and Public Policy, ensuring AI-related grantmaking is coordinated, evidence-based, and consistent with Robin Hood's benefit/cost discipline and standards for measurable results.
- Prioritize among the many demands on our attention, guarding against the risk that resources flow to work that sounds innovative but lacks real potential to help low-income communities.
- Navigate the tensions inherent in AI strategy, weighing AI's potential to improve how organizations and systems serve people against the risks it poses, and help Robin Hood manage tensions and tradeoffs with thoughtfulness, rigor, and judgment.
Build relationships and source opportunity
- Serve as a credible, senior presence in the civic and public-interest technology world, building and stewarding relationships with technologists, funders, tech firms, government, and mission-driven organizations.
- Proactively develop and stay involved in all of Robin Hood's AI-related strategy and partnerships, including teaming with technology companies, co-funders, and donors to Robin Hood, and working closely across our programmatic and development teams to support fundraising efforts with AI and technology companies.
- Cultivate the ecosystem: bring people together through communities of practice and larger convenings such as the AI x Opportunity Summit, and strengthen connections across sectors.
Lead teams and capability
- Oversee Blue Ridge Labs, supporting BRL as a distinct venture and innovation engine, setting direction and aligning its work with Robin Hood's broader AI strategy, while the Director of BRL runs the day-to-day operations and owns results.
- Steward Robin Hood's technical credibility. Robin Hood's technical capacity - business intelligence, data, and IT - sits with the technology team overseen by the Chief Financial & Operations Officer and supports every department. You partner closely with that team to ensure our internal and external AI efforts share a consistent approach and set of principles, serving as their strategic counterpart on AI strategy while they retain ownership of internal delivery, tools, and infrastructure. You are also their strategic counterpart for mission-facing AI questions, and you curate an external advisory bench of tech specialists, so that program staff can assess AI tools and partners well.
- Support decision-making for AI-related grants across Program & Impact teams, serving as connective tissue and a source of guidance, not a bottleneck to decisions.
Ensure responsible, evidence-based practice
- Be accountable for putting Robin Hood's Responsible AI principles into practice - keeping people at the center, grounding decisions in evidence and human judgment, advancing equity and mitigating bias, protecting the communities we serve, and sharing what we learn.
- Ensure the perspectives of people with lived experience of poverty help shape the technology solutions Robin Hood supports.
- Capture and disseminate lessons - including honest reflection on what is not working - to help Robin Hood and the broader field adopt AI in ways that are ethical, effective, and aligned with reducing poverty.
Qualifications
We are looking for someone whose credibility comes from having done the work, not from credentials alone. The strongest candidates will bring most of the following:
• Delivery experience in public-interest or civic technology. You have built, shipped, or led technology work in a mission-driven, government, or community context - and you understand the translation required between technologists and the people who deliver and receive services, and the constrained-resource reality of nonprofits.
• Standing and relationships in the civic-tech ecosystem. You are known and trusted in the world of public-interest technology, and you can open doors to funders, tech firms, government, and peer organizations.
• AI and technology fluency. You use AI tools natively in your own day-to-day work, and are conversant enough in modern AI systems to understand their practical capabilities and limitations, to assess opportunities realistically, and to know when to draw on deeper technical specialists. You bring strong instincts for what's worth building and how to connect technology’s potential to Robin Hood’s mission. You do not need to be a machine-learning engineer.
• Judgment about impact. You can recognize meaningful, 'tech-shaped' social-impact opportunities and distinguish them from novelty; you are comfortable with evidence, measurement, and the discipline of asking whether an intervention actually helps.
• Leadership through influence and management. You can manage a senior leader directly and, just as importantly, move work forward across teams you do not manage, through credibility and collaboration. This role is deliberately designed with a small direct span; your leverage is relational.
• Commitment to equity and to the communities we serve. You believe AI must be shaped with people experiencing poverty, and you center their voices in your work.
• Integrative, systems-level thinking. You understand that AI strategy works best woven through an organization's core approach, not as a silo, and you know how to make that integration real, both within Robin Hood and across grantee organizations and the wider ecosystem.
- Grantmaking or philanthropy fluency is a plus, or the judgment and curiosity to acquire it quickly.
Salary and Benefits
Robin Hood offers a competitive compensation package, with a salary range determined by years of experience, skills, and abilities. Each candidate’s placement within the range will be based on these factors while ensuring alignment with our commitment to equitable internal pay practices, in accordance with the NYC Pay Transparency Law.
The salary range for this role is $190,000 - $240,000 plus bonus potential based on performance.
Our extensive benefits include:
- Medical insurance
- Employer-paid covered dental and vision insurance
- Employer-paid life and long-term disability insurance, plus employer-paid Paid Family Leave premiums
- 403(b) retirement plan with employer match, and a 12% employer contribution to a 401(a) retirement plan
- Employer contributions to Health Spending Account (must be enrolled into the High-Deductible Health Plan)
- Pre-tax flexible spending account (FSA) for health care and dependent care expenses
- Paid time off, including 20 days of accrued PTO (prorated for new hires), plus a separate Sick and Safe Leave bank in accordance with NYC's Earned Safe and Sick Time Act
- Paid Parental Leave and NYS Paid Family Leave
Location
This position is based in New York City and follows a hybrid work environment. Employees are required to work on-site four (4) days per week until further notice. Please note that the hybrid schedule is subject to change based on organizational priorities and team needs; any adjustments will be communicated well in advance.
Equal Employment Opportunity
Robin Hood is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, disability, marital status, sexual orientation, gender identity or expression, veteran status, arrest or conviction record, or any other status protected by applicable federal, state, or local law. Applicants from diverse backgrounds are encouraged to apply.
To Apply
Please visit our website to complete our online application.
Salary Context
This $190K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Robin Hood Foundation, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. Disclosed range: $190K to $240K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Robin Hood Foundation AI Hiring
Robin Hood Foundation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $240K - $240K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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