Interested in this AI/ML Engineer role at International Rescue Committee?
Apply Now →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.
Job Overview
Technology \& AI Adoption Specialist – Operationswill drive the uptake of AI across IRC’s global operations by working directly with teams to identify, test, and scale high\-value use cases. This role is responsible for ensuring AI tools translate into real improvements in how IRC works.
The Specialist will manage a pipeline of AI opportunities across internal functions (e.g., Finance, Supply Chain, HR), embedding with teams to diagnose needs, co\-design solutions, and support adoption through to sustained use. The Specialist acts as the bridge between operational teams (product owners) and technical developers, owning problem definition and user adoption so that development resources are deployed against well\-scoped, high\-value briefs.
Major Responsibilities
AI Demand Pipeline \& Performance Management
- Establish and manage a structured pipeline of AI opportunities across departments, including clear problem definitions, requirements, and expected ROI
- Maintain a shared intake and prioritization process with technical teams, ensuring the pipeline reflects both operational need and development capacity
- Track use cases from intake through pilot, iteration, and scale
- Coordinate with cross\-functional teams to align on development, deployment, and support
- Monitor outcomes and performance against defined success criteria (e.g., efficiency gains, time saved, adoption), providing visibility on progress, key bottlenecks, and lessons learned
AI Use Case Development \& Deployment
- Identify and prioritize high\-impact AI use cases across internal functions, in close collaboration with AI Governance and Risk Transformation Lead and relevant product owners in functional domains
- Work directly with teams to understand workflows, diagnose pain points, and define clear problem statements and requirements suited to AI
- Map current\-state and future\-state processes and translate needs into developer\-ready artifacts (e.g., user stories, workflows) that enable technical teams to build without additional requirements gathering
- Co\-design, test, and iterate solutions with end users and technical teams, including supporting user acceptance testing
- Support AI deployment into live workflows, ensuring solutions are practical, ambitious, adopted, and sustained
- Document and package successful use cases for replication and scale; ensure learnings from each use case deployment improves the next
- Define and maintain appropriate handoff points with development team throughout product lifecycle
Adoption \& Capacity Building (Deployment\-Specific)
- Lead AI adoption within targeted teams, ensuring solutions are embedded into redesigned workflows rather than layered onto existing ones
- Serve as the primary point of contact for end\-users post\-deployment, absorbing support and iteration requests before they reach the development team
- Identify and address barriers to adoption, including resistance, capability gaps, and process misalignment
- Develop practical adoption strategies that drive consistent, confident use
- Design and deliver targeted, role\-specific learning and practical resources (e.g., workflows, prompts, job aids)
- Develop clear, practical communications that translate AI initiatives into accessible, relevant narratives tied to impact
- Create materials such as FAQs, use cases, and success stories that reinforce adoption and learning
Key Working Relationships:
- Position Reports to: (1\) AI Governance and Transformation Lead / Vice President Business Operations \& Analytics, and (2\) Program Technology Innovations Lead
- Key Relationships: AI/Technology development teams, IT, Data, Product Owners in functional departments (Finance, Supply Chain, HR, business development), regional and country teams; Learning \& Development team; Staff \& Culture AI Task Force; Communications teams
Requirements:
- Minimum 6 years of experience in change management, digital transformation, business analysis, learning \& development, or related roles
- Strong familiarity with AI tools and applications, and ability to translate their capabilities and limitations into practical use cases and workflow improvements
- Experience driving adoption of AI and other technology solutions in complex, matrixed organizations, from problem identification through implementation and sustained use
- Strong problem\-solving and analytical skills, including the ability to diagnose operational challenges, define clear problem statements and requirements, and analyze and redesign workflows to embed solutions into day\-to\-day operations
- Experience designing and delivering training or capacity\-building initiatives
- Strong data orientation, including the ability to use data to inform decisions and track impact
- Excellent interpersonal skills and track record of building networks (e.g., champions, communities of practice) to support adoption
- Demonstrated ability to drive alignment and behavior change without direct authority
- Experience working in international development, humanitarian, or nonprofit contexts strongly preferred
- Ability to operate in low\-resource, high\-complexity environments
- Fluency in English required; proficiency in French, Spanish, or Arabic preferred
Working Environment
This position can be based remotely or in an IRC office location. The role requires flexibility to collaborate across multiple time zones and work closely with global teams.
*This is a remote position open to internals candidates based in countries where IRC operates who have the right to work in their location. Successful candidates will be hired on a local employment contract and according to local salary scale.*
*This role is open to candidates located and with the right to work in United States of America, United Kingdom, or Kenya.*
Compensation: (*US Pay Range: $88,277\-$102,451/yr; UK Pay Range: £49,598\-£60,040/yr)* Posted pay ranges apply to US\-based candidates. 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
Compensation: Posted pay ranges apply to US\-based candidates. 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.
US Benefits: We offer a comprehensive and highly competitive set of benefits. In the US, these include: 10 sick days, 10 US holidays, 20\-25 paid time off days depending on role and tenure, medical insurance starting at $163 per month, dental starting at $6\.50 per month, and vision starting at $5 per month, FSA for healthcare and commuter costs, a 403b retirement savings plans with immediately vested matching, disability \& life insurance, and an Employee Assistance Program which is available to our staff and their families to support counseling and care in times of crisis and mental health struggles.
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.
\#li\-1
Salary Context
This $88K-$102K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($95K) sits 56% below the category median. Disclosed range: $88K to $102K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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 - $184K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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