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About This Role
Description
Full Stack Developer — Global Health & AI
ICF’s Global Health and Development (GHD) line of business, part of the Health, People, and Human Services (HPHS) Group, is seeking a Full Stack Developer to build applications that apply AI across global health and survey programs. You will develop applications, APIs, and data services that help shorten survey timelines, reduce costs, and equip national institutions with AI-supported tools. Working with AI engineers, survey experts, designers, and partner-country teams, you will take solutions from prototype to production, including secure cloud applications and offline tools for low-connectivity settings.
This position can be remote within the United States, with occasional international travel.
It is contingent upon award of program funding.
Key Responsibilities
- Build a governed data-access application that lets users explore data in natural language. This includes the authentication, authorization, audit logging, and disclosure-risk controls that make that safe. In this application, access control is a core safeguard for respondent privacy.
- Develop offline-first field tools for interviewers and field supervisors. These tools must work reliably on low-end devices with intermittent connectivity and sync cleanly when a connection returns.
- Integrate AI services into applications, working with the Applied AI Engineer to expose model pipelines through well-designed APIs, including MCP connectors that make DHS data available in AI assistants.
- Spec out and build products with agentic coding tools. Write the specifications that drive agent-led development, then review, test, and own the code that results.
- Design data services and schemas for survey metadata, microdata, and the results of AI-assisted processing.
- Ensure production readiness and sustainable handover. Write testable, well-tested code that passes required quality gates, and produce clear documentation so partner-country institutions can operate and openly publish tools without ongoing ICF support.
- Communicate progress to technical and non-technical stakeholders, including survey experts, program leadership, and partner institutions.
Minimum Required Experience
- Bachelor's degree (or equivalent) with Minimum 5 years, OR
- Master's degree with Minimum 2 years, of professional full stack development experience building production-grade applications
- Minimum 1year of experience integrating LLMs into production systems, with attention to error handling, security, and reliability
- Minimum 1year of experience with a major cloud platform (e.g., AWS, Azure, or Google Cloud), infrastructure as code, and CI/CD
- U.S. citizenship is required by federal government contract
Additional Required Skills and Experience
- Experience with JavaScript/TypeScript and a modern front-end framework (e.g., React, Angular, or Vue)
- Strong back-end development experience in Python or another modern language (e.g., JavaScript/Node.js, Go, Java, C#)
- Experience with relational database design and SQL. Experience with NoSQL technologies is a plus
- Experience designing and integrating RESTful APIs
- Hands-on experience implementing authentication, authorization, and audit logging in a production application
- Demonstrated experience using agentic coding tools (e.g., Claude Code, OpenAI Codex, Cursor, or GitHub Copilot agent mode) to specify, review, test, and deliver production code.
Preferred Skills
- Experience building offline-first or low-connectivity applications
- Experience building a data-access, analytics, or research-data application with role-based access
- Experience working with statisticians or researchers, or with statistical data and workflows
- Understanding of LLM cost, latency, and reliability trade-offs, with exposure to agent frameworks, AI workflow orchestration, MCP servers, or AI evaluation and testing tools.
- Experience building multilingual or localized interfaces
- Experience in global health, international development, or low- and middle-income country settings
Professional Skills
- Builds productive relationships across seniority levels, cultures, and time zones.
- Collaborates closely with designers, product owners, statisticians, and other engineers
- Enjoys wearing multiple hats and taking on varied technical challenges
- Self-motivated. Finds ways to add value without a lot of direct supervision
- Communicates clearly and translates between technical and non-technical stakeholders
- Can simplify complex topics for business and program audiences
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Bot and Third-Party Applications
Please note that this application must be submitted directly by the applicant for consideration. Failure to do so may result in the application being excluded for consideration. Applicants needing an accommodation for disability or religious purposes in connection with the application process should contact [email protected] for assistance.
Working at ICF
ICF is a global advisory and technology services provider, but we’re not your typical consultants. We combine unmatched expertise with cutting-edge technology to help clients solve their most complex challenges, navigate change, and shape the future. We can only solve the world's toughest challenges by building a workplace that allows everyone to thrive. We are an equal opportunity employer. Together, our employees are empowered to share their expertise and collaborate with others to achieve personal and professional goals. For more information, please read our EEO policy.
We will consider for employment qualified applicants with arrest and conviction records.
Reasonable Accommodations are available, including, but not limited to, for disabled veterans, individuals with disabilities, and individuals with sincerely held religious beliefs, in all phases of the application and employment process. To request an accommodation, please email [email protected] and we will be happy to assist. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
Read more about workplace discrimination rights or our benefit offerings which are included in the Transparency in (Benefits) Coverage Act.
Candidate AI Usage Policy At ICF, we are committed to ensuring a fair interview process for all candidates based on their own skills and knowledge. As part of this commitment, the use of artificial intelligence (AI) tools to generate or assist with responses during interviews (whether in-person or virtual) is not permitted. This policy is in place to maintain the integrity and authenticity of the interview process.
However, we understand that some candidates may require accommodation that involves the use of AI. If such an accommodation is needed, candidates are instructed to contact us in advance at [email protected]. We are dedicated to providing the necessary support to ensure that all candidates have an equal opportunity to succeed.
Pay Range - There are multiple factors that are considered in determining final pay for a position, including, but not limited to, relevant work experience, skills, certifications and competencies that align to the specified role, geographic location, education and certifications as well as contract provisions regarding labor categories that are specific to the position.
The pay range for this position based on full-time employment is:
$81,499.00 - $138,549.00 Nationwide Remote Office (US99)
Salary Context
This $81K-$138K range is in the lower quartile 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 ICF, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $81K to $138K.
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.
ICF AI Hiring
ICF has 7 open AI roles right now. They're hiring across MLOps Engineer, AI Product Manager, Data Scientist, AI/ML Engineer. Positions span Reston, VA, US, Montgomery, AL, Rockville, MD, US. Compensation range: $125K - $184K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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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