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Job Title: AI Integration Director
Job Type: Full Time
Reports to: Chief Academic Officer
School Website: http://www.waysideschools.org
Contract Days: 220 Days
Salary: $80,000
This position is grant\-funded.
Job Goal: At Wayside Schools, we seek staff with diverse backgrounds, experiences, perspectives, talents, and ideas to meet the needs of our growing body of scholars and dynamic programming. At Wayside, we know that leaders who possess specific traits and competencies are more likely to be successful in their roles and in their service to staff, scholars, and families: passion for our mission, a deep connection to issues of social justice, a commitment to all scholars we serve, and a focus on personal growth and development.
The AI Integration Director will serve as a key partner for the Chief Academic Officer and campus instructional leaders, working to integrate artificial intelligence tools and applications into academic functions across elementary and secondary campuses. The AI Integration Director is responsible for supporting campuses in the use of AI for unit planning, lesson planning, and the development of AI applications that solve academic problems of practice in the classroom. The AI Integration Director will approach this work through direct campus support, professional development, and collaboration with academic leaders to ensure that AI tools are implemented with fidelity and aligned to Wayside’s instructional vision.
This is an ideal opportunity for a dynamic and innovative instructional leader with a strong background in curriculum and instruction who is eager to be at the forefront of AI integration in education. The AI Integration Director will play a critical role in shaping the future of teaching and learning in a premier PK\-12 charter management organization committed to growing scholars who will become college graduates, successful, well\-rounded professionals, and engaged community members.
Education and Certification Requirements:
- Bachelor’s Degree in an education\-related field required
- Master’s Degree preferred
- Teaching certification preferred but not required
- Administrative certification preferred but not required
Skill Requirements:
- Highly developed communication and facilitation skills
- A high degree of organization and self\-direction
- Able to work with a variety of online systems and digital tools
- A strong understanding of curriculum and instruction at the elementary and/or secondary level
- Ability to identify academic problems of practice and develop solutions using AI tools
- An entrepreneurial spirit and demonstrated success in building or launching a new program, initiative, or system
- Outstanding written, speaking, and organizational skills, including excellence in backwards planning, goal setting, and progress monitoring
- The flexibility needed to accommodate the breadth and depth of responsibilities across multiple campuses
- Ability to interact with a broad range of stakeholders with different interests and needs, including teachers, instructional coaches, and campus leaders
- Deep commitment to improving the lives of scholars from low\-income communities
- Demonstrated passion for the mission, vision, and values of Wayside Schools
- Alignment with Wayside’s commitment to provide access to rigorous and well\-balanced academic programming
- High proficiency in Microsoft Excel, PowerPoint, Word, and Google Suite
- Openness to learning and applying new technologies, including AI platforms and applications
Note: Prior experience building AI applications is not required. Training on building AI applications is provided to the applicant hired for this position. Coding knowledge is not required. Applications are built by writing prompts in English.
Experience Requirements:
- Minimum of 3 years of classroom teaching experience required
- Experience as an instructional coach, curriculum specialist, or campus leader preferred
- Experience with curriculum design, unit planning, and lesson planning required
- Experience with AI tools in an educational setting is preferred but not required
- Experience with using data to improve instructional outcomes is required
Performance Responsibilities:
I. AI Integration and Application Development
- Build AI applications to solve academic problems of practice at the classroom, campus, and network level
- Identify problems of practice in collaboration with campus instructional leaders and develop AI\-based solutions to address them
- Support elementary and secondary campuses in the implementation of AI\-created unit plans and lesson plans
- Ensure that AI tools and applications are implemented with fidelity and aligned to Wayside’s instructional priorities
- Continuously evaluate the effectiveness of AI applications and refine them based on campus feedback and student outcome data
- Stay current on emerging AI tools and platforms relevant to K\-12 education and bring recommendations to the CAO and Academic Leadership Team
II. Campus Support and Professional Development
- Support elementary and secondary campuses as they build and use AI applications to solve problems of practice in the classroom
- Provide training to campus leaders, instructional coaches, and teachers on how to build and use AI applications
- Facilitate professional development sessions on AI integration for academic functions, including unit planning, lesson planning, and tutoring/centers
- Serve as a thought partner and resource for campus instructional teams as they integrate AI tools into their daily instructional practice
- Provide ongoing, job\-embedded coaching and support to campus teams during the implementation of AI tools
- Develop and maintain training materials, guides, and resources to support campus\-level AI integration
III. Collaboration and Academic Alignment
- Collaborate with the CAO to align AI integration efforts with Wayside’s academic priorities and strategic plan
- Partner with campus principals, assistant principals, and instructional coaches to identify opportunities for AI integration within existing academic systems
- Participate in the Network Academic Leadership Team to contribute to the development of priorities, action plans, and strategies for AI integration across all Wayside Schools
- Support the implementation of AI tools within Wayside’s academic systems, including unit planning, lesson planning, data analysis, and scholar tutoring and centers
- Collaborate with the Network Support Team to ensure that the technology infrastructure supports AI integration efforts on all campuses
IV. Monitoring, Reporting, and Continuous Improvement
- Collect and analyze data on the implementation and impact of AI tools across campuses
- Prepare regular progress reports for the CAO on the status of AI integration efforts and key outcomes
- Recommend system\-wide goals for AI integration and monitor progress toward achieving those goals
- Support campuses in using AI\-generated data and insights to inform instructional decisions and improve scholar outcomes
- Document and share best practices and success stories from campuses to promote a culture of innovation and continuous improvement
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Wayside Schools, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Wayside Schools AI Hiring
Wayside Schools has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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