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.
Job Background / Overview
The IRC is actively investing in enterprise AI to improve the speed, quality, and scale of its programs and operations. As the organization expands its use of AI platforms, including tools from Anthropic, OpenAI, and integrated data environments, the IT department is building dedicated technical capacity to keep these systems running reliably, securely, and effectively.
The IRC is investing in enterprise AI to improve the speed, quality, and scale of its programs and operations. As that investment grows, so does the need for someone to own the technical layer underneath it: the systems, integrations, and infrastructure that make AI tools work at scale.
The AI Platform Engineer is that person. This is a technical operations role, not a user-facing one. This role is responsible for keeping the IRC's enterprise AI environment running managing platform configurations and API access, building and maintaining integrations between AI tools and IRC systems, writing scripts and automations, and serving as the technical point of contact for data engineering and InfoSec. Day-to-day, this person is more likely to be reading logs, troubleshooting a broken pipeline, or pushing a configuration change than answering questions from end users.
The role works in close coordination with the AI Platform Support Director, who owns vendor relationships, licensing, and adoption strategy. This role owns the technical execution underneath that, ensuring the systems are reliable, observable, and properly connected to the rest of the IRC's data and operational environment.
Major Responsibilities
AI Systems Administration & Operations (40%)
- Serve as primary technical administrator across IRC enterprise AI environments, currently including Anthropic (Claude) and OpenAI platform deployments
- Manage user access, API key governance, workspace configurations, and environment-level settings across AI platforms
- Monitor system health, usage patterns, and API performance across AI tools; triage and resolve operational issues as they arise
- Maintain and improve observability across AI systems
- Tracking uptime, error rates, token consumption, and integration reliability
- Oversee and document configuration changes, environment updates, and deployment procedures across managed platforms
- Support responsible use by flagging anomalous usage patterns and coordinating with InfoSec on policy adherence and access controls
Integrations & Technical Implementation (35%)
- Coordinate with the DevOps, SW Engineering and Data Engineering team(s) on deployment processes, environment access, and infrastructure dependencies required to build and maintain AI integrations
- Follow established change management procedures for all configuration changes, environment updates, and integration deployments, including documentation, testing, and appropriate approvals before pushing to production
- Develop lightweight scripts, connectors, and automations to support AI-assisted workflows across teams, primarily in Python and/or JavaScript/TypeScript
- Troubleshoot integration failures, data flow issues, and API connectivity problems across the AI ecosystem
- Collaborate with the data engineering team on AI/KM pipeline work, including vector store ingestion, retrieval configuration, and source data connections
- Contribute to technical design discussions with engineering partners, translating operational requirements into implementable solutions
- Maintain technical documentation for all integrations, including architecture notes, runbooks, and dependency maps
Monitoring, Resource Optimization & InfoSec Liaison (15%)
- Track and report on AI resource utilization across platforms, identifying opportunities to reduce waste and improve cost efficiency in coordination with the AI
- Serve as the technical point of contact with the InfoSec team on matters related to AI system security, data handling, access controls, and compliance requirements
- Support risk assessments and security reviews for new AI tools or integrations by providing accurate technical context on system behavior and data flows
- Contribute to the development of technical SOPs and best-practice guidelines for AI system use, in coordination with the AI Platform Support Director and relevant stakeholders
Stakeholder Support & Collaboration (10%)
- Act as a technical resource for program and operations teams adopting AI tools, including answering implementation questions, supporting troubleshooting, and identifying configuration solutions
- Participate in rollout planning for new AI capabilities, providing grounded input on technical feasibility, integration requirements, and operational readiness
- Collaborate with the AI Platform Support Director on onboarding documentation and technical guidance materials for end users
- Contribute to sprint and project planning with accurate estimates on technical effort and dependencies
Required Experience & Skills
AI & Cloud Platforms
- Hands-on experience administering enterprise AI platforms (Anthropic, OpenAI, Azure OpenAI, or comparable tools), including API management, access controls, and environment configuration
- Familiarity with LLM application infrastructure: prompt pipelines, Model Context Protocol (MCP), other tool-calling integration frameworks, vector databases, retrieval-augmented generation (RAG) patterns, and embedding workflows
- Experience working with Databricks or comparable data/ML platforms is a strong plus
Integration & Development
- Proficiency in Python and/or JavaScript for scripting, automation, and lightweight integration work
- Experience building and maintaining REST API integrations, including authentication patterns, webhook handling, and error management
- Comfort reading and working within existing codebases without requiring significant architectural guidance
- Familiarity with version control (Git) and standard deployment practices for scripts and integrations
Systems Administration & Monitoring
- Experience monitoring distributed systems or SaaS platforms, including setting up alerting, reviewing logs, and diagnosing performance or availability issues
- Familiarity with usage/cost monitoring for cloud or API-based services
- Comfort operating in live production environments where reliability and data integrity are critical
Security & Compliance
- Working knowledge of information security principles as they apply to SaaS and API-based systems: access controls, credential management, data handling, and audit logging
- Ability to engage constructively with InfoSec teams, providing clear technical context to support reviews and risk assessments
Collaboration & Communication
- Ability to communicate technical concepts clearly to non-technical colleagues and program staff
- Experience contributing to cross-functional teams alongside product, engineering, and operations stakeholders
- Strong documentation habits: runbooks, SOPs, architecture notes, and internal guides
Working Environment
This role is remote, with the possibility of in-person work depending on location. Stakeholders are located across multiple time zones (GMT-8 to GMT+3), which may require occasional early or late meetings.
Compensation: ($88,277 - $102,451/annual). 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
Benefits: IRC offers a comprehensive and competitive benefits package. Highlights include 10 paid sick leave, 10 U.S. holidays, 20-25 generous paid time off based on role and tenure, medical insurance starting at $163/month, dental starting at $6.50/month, and vision starting at $5/month, flexible spending accounts (healthcare, dependent care, and commuter), a 403(b) retirement savings plan with employer match (immediate vesting) along with a non-elective employer contribution based on age and years of service (3-year vesting), disability and life insurance, and an Employee Assistance Program (EAP) offering confidential counseling and support services for employees and their families.
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.
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Salary Context
This $88K-$102K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($95K) sits 55% below the category median. Disclosed range: $88K to $102K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
International Rescue Committee AI Hiring
International Rescue Committee has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $102K - $102K.
Location Context
AI roles in New York pay a median of $221,000 across 3,257 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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