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About This Role
Director, AI for Financial Health Strategy
Full-Time, 18-Month Engagement with Full Benefits
SCOPE OF ROLE
At the Financial Health Network, we have always harnessed the power of technology in service of our mission, improving financial health for all. As the landscape of Artificial Intelligence (AI) rapidly evolves, it presents a tremendous opportunity to deliver personalized, real-time, and scalable financial guidance to the millions of consumers living paycheck to paycheck. Within our 2024-2026 strategic plan, we have prioritized advancing AI as a navigator for the financially vulnerable. We are now seeking visionary expertise and leadership to lead and drive our efforts in AI and financial advice, specifically to explore the potential, inform the design, and help scale AI navigation tools that empower the financially vulnerable.
The Director of AI for Financial Health is a full-time, fixed-term leadership position structured as an 18-month engagement with full employee benefits. An extension or conversion to a
permanent role may be considered based on organizational needs and mutual agreement at the conclusion of the initial term.
The Director will lead the development and execution of Financial Health Network’s strategic priority to leverage Artificial Intelligence (AI) in advancing financial health through innovative
financial advice solutions. This leader will oversee insight generation, strategy development, external partnerships, fundraising, and project implementation to drive this priority forward. The Director will serve as a member of the Program Team (PT) and report to the Chief Program Officer (CPO).
EDUCATION
Bachelor’s degree (B.A. or B.S.) in Public Policy, Social Science, Economics, Computer Science, or relevant field required; advanced degree preferred or equivalent experience
LOCATION
Chicago IL, Washington DC, or Remote
SALARY RANGE
$130,000- $140,000 (Annual)
The skills and experience of qualified candidates will determine the exact compensation for which this job is filled.
REQUIRED QUALIFICATIONS
- 10+ years of relevant experience, including working at the intersection of technology and financial health. Experience in designing, marketing, and/or evaluating AI tools.
- Expertise in AI systems and knowledge of design principles (i.e., natural language
processing (NLP), machine learning, predictive analytics, personalization) paired with subject matter knowledge in consumer finance, financial health, or financial inclusion,
and a strong grounding in ethical AI practices, including data privacy, algorithmic transparency, and fairness.
- Demonstrated ability to translate the role of technology into practical solutions to advance financial health.
- Ability to build and leverage a strong network across the AI and financial services landscape, including with technology companies, nonprofits, and funders, resulting in
innovative, scalable solutions and new opportunities for partnerships, business development, and grants.
- Comfortable using industry knowledge, consumer and provider insights, and data to make strategic recommendations for action.
- Excellent communicator with advanced executive presentation skills; can translate complex technical concepts into actionable insights for diverse audiences, including
executives, funders, and external partners.
PREFERRED QUALIFICATIONS
- Experience mentoring and leading cross-functional teams.
- Experience in business development and/or fundraising.
RESPONSIBILITIES
*Strategy development and execution*
- Be a strategic thought leader, connecting emerging technology trends to our mission and organizational goals (OKRs).
- Lead and refine our strategy on AI and financial advice, in close partnership with the Chief Program Officer and other senior leaders.
- Oversee the execution and ensure the quality delivery of our AI initiative(s), in collaboration with other Program or Research Team colleagues.
- Partner with colleagues across FHN to gather input and engage members and external partners in advancing our AI work.
- Apply AI expertise internally to enhance operational efficiency across functions such as recruiting, business development, and other core processes.
*Relationship management*
- Cultivate and expand strategic relationships with providers, funders, peers, and researchers advancing the use of AI to improve financial health.
- Identify and pursue business development and grant fundraising opportunities to support revenue generation for our AI initiatives, in partnership with Development colleagues,
business leaders, and members of the Leadership Team.
- Serve as the primary point of contact for key stakeholders engaged in our AI and financial health work.
*Thought leadership and external presence*
- Partner with the Research Team to generate insights that inform FHN’s perspective on AI and financial advice, leveraging research produced by FHN and partner
organizations.
- Develop and iterate on our point of view, collaborating with the Marketing & Communications team and other colleagues to develop a cohesive narrative for our AI
work.
- Drive thought leadership efforts by authoring blogs, contributing to social media content, briefs, and other publications that elevate FHN’s voice in the field.
- Identify and engage with key platforms and events where FHN should maintain a visible presence; represent the organization through speaking engagements and
other opportunities to strengthen FHN’s position in the broader ecosystem.
ABOUT FINANCIAL HEALTH NETWORK
Make an impact in the financial lives of millions with our growing organization. The Financial Health Network is the leading authority on financial health. We envision a future where all
people, especially those who are most vulnerable, have the day-to-day financial systems they need to be resilient and thrive. We are a trusted resource for business leaders,
policymakers and innovators united in a mission to improve the financial health of their customers, employees and communities. Through groundbreaking research, advisory services,
measurement tools, and opportunities for cross-sector collaboration, we advance awareness, understanding and proven best practices in support of improved financial health for all. With
offices in Chicago and Washington, DC, Financial Health Network is a highly collaborative environment, seeking people who share our values.
At Financial Health Network our mission to improve financial health for all, especially the most vulnerable, can only be accomplished through a diverse population of employees at all levels who are representative of the many communities we seek to impact. We strive to recruit, hire, develop, empower, and retain talented individuals at all levels across various dimensions of diversity and intersectionality including, but not limited to, race, ethnicity, sexual orientation, gender identity, gender expression, religion, age, neurodiversity, disability status, veteran status, and citizenship. We welcome and need your unique voice and look forward to having you join our team!
We will provide equal employment opportunities to all applicants without regard to an applicant’s race, color, religion, sex, sexual orientation, gender identity or expression, gender, genetic information, uniformed service, national origin, age, veteran status, disability, pregnancy, or any other status protected by federal or state law. We will provide reasonable accommodations to allow an applicant to participate in the hiring process (e.g., accommodations for a job interview) if so requested.
TO APPLY
Our mission is very important to us, and we would like to know why you would like to be a part of the Financial Health Network. Please submit a compelling cover letter and resume with
your application.
Salary Context
This $130K-$140K range is below the median for AI/ML Engineer roles in our dataset (median: $170K across 217 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 37,339 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Financial Health Network, 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 $154,000 based on 8,743 positions with disclosed compensation. Director-level AI roles across all categories have a median of $230,600. This role's midpoint ($135K) sits 12% below the category median. Disclosed range: $130K to $140K.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
Financial Health Network AI Hiring
Financial Health Network has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $140K - $140K.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 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 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 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 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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