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About This Role
About Us
Girl Scouts of the USA
Founded in 1912, Girl Scouts of the USA (GSUSA) is the preeminent leadership development organization for girls and the leading authority on their healthy development. Millions of Girl Scout alums across the country and the world have gone on to achieve incredible things and make a tremendous impact in fields ranging from public policy, medicine, journalism, and athletics to international relations, science, technology, art, and entertainment.
As a mission-driven organization, Girl Scouts of the USA champions girls as they explore their worlds, try new things, and develop the courage, confidence, and character to make the world a better place. From stargazing during a first overnight camping trip to building robots, navigating a rocky hiking trail, working together on a community project, and learning about the importance of environmental stewardship, Girl Scouts of all backgrounds and abilities can unapologetically be themselves as they rise to meet new challenges and discover the issues that matter most to them. Along the way they learn the power of friendship, connection, and teamwork.
Girl Scouts are doers, innovators, problem-solvers, creators, and dreamers, and the GSUSA team is here to center, support, and inspire them, working with 111 local councils across the country and USA Girl Scouts Overseas to deliver our life-changing Girl Scout Leadership Experience.
The Technology community sets technology vision, strategy, goals, and performance measures to fulfill and advance the Technology Council Offerings that support the Movement’s business objectives. Technology directs traditional information technology functions, as well as enhances business value with innovative, technology-based solutions.
#li-remote You Will
The Vice President, Business Process Engineering and Applied Agentic AI is responsible for the planning, design, implementation, and ongoing operation of GSUSA’s enterprise technology platforms, including externally facing systems, infrastructure, and emerging AI-driven capabilities.This role establishes the foundational AI platform, Agent Builder Framework, and enterprise process automation strategy to enable GSUSA’s transition to an agentic operating model, while ensuring reliability, scalability, and alignment with core business priorities.
The VP provides leadership and oversight to define technology architecture, guide platform engineering, and ensure operational excellence across systems, integrations, and vendor-delivered solutions. This includes responsibility for AI-enabled workflows, as well as coordination with external managed service providers (MSPs) to deliver consistent, high-quality solutions aligned with GSUSA standards. This leader serves as a key advisor to senior leadership, ensuring technology investments are innovative, cost-effective, secure, and aligned to the organization’s mission, financial constraints, and strategic goals. Essential Responsibilities
Technology Strategy & Leadership
- Partner with the Chief Technology Officer and Technology leadership to define and implement enterprise technology strategy for AI platform, Applied AI and process automation initiatives
- Establish policies, standards, and governance for scalable, secure, and cost-effective technology delivery
- Align technology investments with GSUSA mission, business priorities, and financial constraints
Key Programs and Outcomes
- Enterprise AI Agent Builder Framework (v1)
- Business Process Automation & AI Transformation Strategy
- Process Engineering Roadmaps (domain-specific transformation plans)
- Reference Architecture for Agentic Systems (AI, data, integration, and UI)
- MSP Enablement Standards (AI Framework, UI/UX, and integration guidelines)
- Platform reliability, performance, and operational excellence metrics
AI Platform & Agent Builder Framework
- Design and define the enterprise Agent Builder Framework for creating, deploying, and managing AI agents
- Establish standards for agent orchestration, tool integration, memory, and governance
- Develop reusable components, templates, and reference architectures for scalable agent development
- Serve as the central design authority for agentic AI and early-stage AI transformation initiatives
Business Process Automation & Engineering
- Develop and lead the enterprise-wide business process automation and process engineering strategy for AI-enabled workflows
- Identify and prioritize high-value automation opportunities across business domains
- Define human-in-the-loop models and decisioning frameworks for agent-assisted workflows
- Create structured process engineering roadmaps aligned to transformation priorities
Platform Engineering & Architecture
- Establish foundational architecture for AI platform services, orchestration layers, and enterprise systems
- Define integration patterns across CRM/ERP systems, cloud platforms, and AI services
- Ensure alignment between front-end experience layers and backend AI and data platforms
External MSP & Vendor Management
- Manage relationships with external technology vendors and managed service providers (MSPs)
- Define standards for UI/UX, APIs, and integration for externally delivered solutions, including React-based applications
- Ensure vendor-delivered products are on time, within budget, and aligned with specifications and architecture standards
- Provide technical expertise to procurement efforts and vendor negotiations
Culture, Enablement & Change Leadership
- Foster a high-performing engineering culture focused on innovation, collaboration, and continuous improvement
- Promote knowledge sharing and technical learning across the organization
- Lead organizational change initiatives supporting adoption of AI and automation-driven ways of working
- Enable internal teams and partners to effectively adopt new frameworks and platforms
- Support inclusive practices in technology design, development, and delivery
- Ensure alignment with GSUSA’s affirmative action and nondiscrimination commitments
Note: This job description is not intended to be an exhaustive list of all duties, responsibilities, or activities required of the role. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the position.
Occasional evening or weekend work may be required
Required Competencies
- Proven leadership experience managing large-scale technology transformation initiatives
- Ability to bridge business strategy and complex technical architecture
- Leadership ability to influence diverse stakeholders and drive organization-wide change
Required Technical Skills
- Enterprise systems: Salesforce, SAP, Oracle/NetSuite, and web-based platforms
- Cloud platforms: AWS, Azure, Google Cloud
- Integration technologies: MuleSoft, RESTful APIs, GraphQL, microservices architectures
- Identity and data platforms: Okta, Gigya, SQL/NoSQL databases
- AI/ML concepts, orchestration platforms, and agent-based architectures
You Need
- Bachelor’s degree in Computer Science, Information Management, or related field (or equivalent experience)
- Strong preference for advanced degree (MS/MBA) or equivalent leadership experience
- 10+ years in senior-level management roles with multidisciplinary responsibility
- 15+ years of experience in enterprise information technology leadership
- Proven leadership experience managing large-scale technology transformation initiatives
- Deep experience driving enterprise platform modernization and AI-enabled transformation
- Strong fiscal responsibility and experience managing enterprise technology budgets
SALARY RANGE: $109,000 - $260,000 This represents the present low and high end of the pay range for this position. This pay range is only applicable to NYC Metro-based or similarly situated employees. Actual pay will vary based on various factors, including but not limited to experience.
What We Offer: Girl Scouts is powered by people, and we encourage our team members to be their best selves in and out of the office. We place a high priority on flexibility and offer a competitive employee salary and benefits package that includes:
Paid Time Off: GSUSA offers 20+ days of paid time off, 2 floating holidays, as well as 9 workplace holidays per year. GSUSA staff also enjoy a paid holiday year-end office closure between Christmas and New Year's.
Other Benefits:
- Medical and Behavioral Health Coverage + Plan options with individual and family coverage which includes wellness, hospitalization, and fertility assistance. + Both plans include GSUSA partial subsidy of premium costs + Dental and vision coverage
- Health Savings Accounts (HSAs) and Flexible Spending Accounts (FSAs) including Health, Dependent Care, and Limited FSA for those with Health Savings Accounts
- Company-paid life insurance
- Flexible work arrangements
- 12 weeks of paid parental leave
- 401(K) with company match
- Sick leave
- Short- and Long-Term Disability for salary continuation
- Health and Wellness Classes and Activities throughout the year
- Access to Employee Engagement Groups
PHYSICAL DEMANDS: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.
WORK ENVIRONMENT: This job is remote.
POSITION TYPE / EXPECTED HOURS OF WORK: This is a full-time position. Days and hours of work are Monday through Friday. Occasional evening and weekend work may be required as job duties demand.AAP/EEO Statement: GSUSA is an equal employment opportunity employer.
Travel Requirements
0-10%
Salary Context
This $109K-$260K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Girl Scouts of the USA, 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 11,857 positions with disclosed compensation. This role's midpoint ($184K) sits 13% below the category median. Disclosed range: $109K to $260K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Girl Scouts of the USA AI Hiring
Girl Scouts of the USA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $260K - $260K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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