Interested in this AI/ML Engineer role at International Rescue Committee?
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About This Role
The International Rescue Committee (IRC) responds to the world's worst humanitarian crises, helping to restore health, safety, education, economic wellbeing, and power to people devastated by conflict and disaster. Founded in 1933 at the call of Albert Einstein, the IRC is one of the world's largest international humanitarian non-governmental organizations (INGO), at work in more than 40 countries and 29 U.S. cities helping people to survive, reclaim control of their future and strengthen their communities. A force for humanity, IRC employees deliver lasting impact by restoring safety, dignity and hope to millions. If you're a solutions-driven, passionate change-maker, come join us in positively impacting the lives of millions of people world-wide for a better future.
The International Rescue Committee (IRC) responds to the world's worst humanitarian crises, helping to restore health, safety, education, economic wellbeing, and power to people devastated by conflict and disaster. Founded in 1933 at the call of Albert Einstein, the IRC is one of the world's largest international humanitarian non-governmental organizations (INGO), at work in more than 40 countries and 29 U.S. cities helping people to survive, reclaim control of their future and strengthen their communities. If you're a solutions-driven, passionate change-maker, come join us in positively impacting the lives of millions of people worldwide for a better future.
Background:
Build the AI systems that will reach hundreds of millions of people in the world's hardest places.
Nearly 240 million people will need humanitarian aid in 2026, and global funding has fallen by more than half. Closing that gap demands a different kind of humanitarian organization — one that uses AI to multiply what frontline workers can do, reaches people before crises hit, and brings diagnostic and decision-support tools to places the technology industry has historically ignored.
The International Rescue Committee (IRC) is already one of the most ambitious AI-deploying humanitarian organizations in the world. We are working on dozens of AI initiatives across 30+ countries: agentic systems serving millions of displaced people through Signpost; anticipatory action models that deliver aid in advance of a disaster; pocket-sized AI diagnostics and AI teaching mentors. We have strategic partnerships with AI partners and a portfolio of programs ready to scale.
We are hiring a Head of AI Engineering to lead the technical team that builds these systems — and to help shape what we build next.
Job Overview:
This is a senior technical leadership role for someone who wants to build at the frontier of applied AI in service of humanitarian impact. You will lead a team of engineers and AI specialists designing and shipping AI-powered, program-facing solutions — primarily agentic systems, predictive analytics, and AI diagnostics deployed in low-resource and high-stakes settings.
You will own three things:
Technical strategy and direction for IRC's program-facing AI portfolio. You will help translate IRC's AI ambitions into a clear technical roadmap, make build-vs-buy-vs-partner decisions, and set the architectural direction for systems that need to work reliably in some of the hardest operating environments on earth — including AI that runs offline on low-cost smartphones, agentic systems with high safety standards, and diagnostic models trained on populations the major datasets ignore.
A high-performing engineering team. You will manage and grow a team of engineers, applied AI engineers, and QA specialists; raise the bar on engineering quality; and build the kind of culture that attracts top technical talent to mission-driven work.
Deep partnership with program leaders and strategic partners. You will be a true thought partner to IRC's program experts in health, education, protection, resettlement, and emergency response — co-designing solutions with them rather than receiving requirements. You will also help us get maximum value from our flagship partnerships with Anthropic, OpenAI, and other frontier labs, with direct access to frontier models, early features, and the researchers building them.
Major Responsibilities
AI Strategic Leadership
- Translate IRC's AI strategy and program priorities into a prioritized technical roadmap, in close partnership with program leaders, the SVP for Research, Innovation, AI, and People & Culture, and the T4P demand pipeline.
- Stay at the edge of AI/ML advances — particularly in agentic systems, multimodal models, and edge/on-device inference — and bring promising approaches to IRC's programs.
- Serve as a core member of IRC's AI leadership, helping shape organizational direction alongside the SVP for Research, Innovation, AI, and People & Culture and other senior leaders.
- Champion responsible, human-centered AI deployment in some of the most sensitive humanitarian contexts in the world, with the safety standards those contexts demand.
- Represent IRC in technical discussions with partners, peer organizations, donors, and the broader humanitarian and AI sectors.
Team Leadership and Management
- Manage and develop a team of engineers, applied AI engineers, and QA specialists. Hire well, raise the bar, and build a culture that strong technical people want to be part of.
- Establish engineering quality standards, development practices, and documentation protocols.
- Maintain a prioritized backlog of program innovations and manage team capacity against demand.
- Mature the team's AI development and DevOps practices and grow stakeholder buy-in across the organization.
- Identify opportunities to secure budget resources in support of innovation priorities.
Cross-functional Partnership
- Act as a genuine thought partner to program leaders — bringing AI fluency to their problems and humility about the field realities
- Co-design solutions that actually work in the field, not technology-first answers in search of problems.
- Partner with the Enterprise Applications team to ensure smooth handoffs of proven innovations for enterprise-wide scale-up.
- Coordinate with the Enterprise Applications DevOps team on integration touchpoints, infrastructure dependencies, and shared engineering standards.
Flagship AI Partnerships
- Serve as the technical lead on IRC's strategic partnerships with frontier AI organizations — helping turn early relationships into long-term, high-leverage collaborations.
- Identify where partnership access (compute, frontier models, technical expertise) can unlock outsized program impact.
Solution Design and Engineering
- Lead the design and development of AI-driven and program-specific technology solutions, owning the full development lifecycle from requirements through handoff.
- Interface with program and business stakeholders to gather requirements, define technical solutions, and recommend approaches grounded in a detailed understanding of program goals.
- Design and oversee high-availability architectures in Microsoft Azure, incorporating appropriate redundancy, security, backup, and recovery strategies — including for sensitive humanitarian data.
- Research and recommend technology products, services, and standards — particularly in AI/ML — to support procurement, development, and strategic decision-making.
DevOps and Engineering Operations
- Oversee deployment pipelines, CI/CD automation, infrastructure-as-code, and QA processes to ensure efficient and reliable software delivery.
- Ensure ongoing availability and performance of applications through monitoring, scaling, and proactive operations management.
- Track and optimize cloud and model-inference costs.
- Maintain technical and policy documentation for all assets under the team's management.
Key Working Relationships
- Position Reports to: Chief Information Officer, working in close partnership with the SVP for Research, Innovation, AI, and People & Culture
- Position directly supervises: Engineers, applied AI engineers, ML specialists, and QA staff on the T4P Development team.
- Other internal contacts: Program leaders across CRRD, RAI, R&I, and Signpost; the Enterprise Applications and DevOps teams; the AI governance and policy team; AI@Scale (I@S) leadership; IT leadership across regions and HQ.
- External contacts: Strategic partners with leading AI organizations; technology vendors; AI sector peers; NGO tech community; major foundation funders.
Job Requirements
Strong candidates will bring most — not necessarily all — of the following:
AI and engineering depth
- Hands-on experience building and shipping production AI systems — agentic frameworks, LLM applications, RAG systems, computer vision, or applied ML pipelines.
- Solid full-stack foundation (Python, JavaScript/TypeScript, and modern frameworks) with the technical credibility to engage directly with engineers on architecture and design decisions.
- Experience with cloud architecture (Azure preferred; AWS/GCP transferable), including AI services, DevOps pipelines, and cost-effective scaling.
- Working knowledge of data security and privacy practices for sensitive or personally identifiable information.
Leadership and collaboration
- 7–10+ years in software engineering or technical product development, with 3+ years leading engineers as a manager or tech lead.
- A track record of building or growing strong engineering teams — hiring, raising the bar, and creating a culture people want to be part of.
- Ability to engage non-technical leaders as a peer: explaining trade-offs clearly, listening well, and translating between worlds.
- Comfort operating across global teams, time zones, and contexts very different from your own.
Mindset
- Genuinely motivated by humanitarian impact
- Leads with humility. Deep respect for the people closest to the problem, including those without technical backgrounds.
- Technically curious and forward-looking — reads, experiments, and brings new ideas to the team.
Nice to have
- Experience deploying AI in low-resource, low-bandwidth, or offline contexts.
- Prior work with humanitarian, development, or global health organizations.
- French or Arabic language skills.
Working Environment
Standard office work environment; hybrid/remote available. Some travel may be required for partner meetings, field engagement, and sector conferences.
Compensation: (US Pay Rate: 185,000-200,000 USD); (UK Pay Rate: 87,000-100,000 GBP) Ranges are based on various factors including the labor market, job type, internal equity, and budget. Exact offers are calibrated by work location, individual candidate experience and skills relative to the defined job requirements.
Professional Standards: All International Rescue Committee workers must adhere to the core values and principles outlined in IRC Way - Standards for Professional Conduct. Our Standards are Integrity, Service, Equality and Accountability. In accordance with these values, the IRC operates and enforces policies on Safeguarding, Conflicts of Interest, Fiscal Integrity, and Reporting Wrongdoing and Protection from Retaliation. IRC is committed to take all necessary preventive measures and create an environment where people feel safe, and to take all necessary actions and corrective measures when harm occurs. IRC builds teams of professionals who promote critical reflection, power sharing, debate, and objectivity to deliver the best possible services to our clients.
Cookies: https://careers.rescue.org/us/en/cookiesettings
Equal Opportunity Employer: IRC is an Equal Opportunity Employer. IRC considers all applicants on the basis of merit without regard to race, sex, color, national origin, religion, sexual orientation, age, marital status, veteran status, disability or any other characteristic protected by applicable law.
Salary Context
This $185K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At International Rescue Committee, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($192K) sits 9% below the category median. Disclosed range: $185K to $200K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
International Rescue Committee AI Hiring
International Rescue Committee has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $102K - $200K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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