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REPORTS TO: Chief Technology Officer
POSITION OVERVIEW:
The Director of AI \& Emerging Technology will lead the strategy, governance, and implementation of artificial intelligence across Boston Public Schools. This role is responsible for identifying high\-impact opportunities for AI, driving pilot initiatives, and scaling solutions that support teaching, learning, and district operations.
Operating as a cross\-functional leader, the Director will partner with academic leadership, technology teams, data and accountability, legal, and operations to ensure AI is implemented effectively, responsibly, and in alignment with district priorities. This role establishes clear guardrails for the use of AI, ensures protection of student and staff data, and aligns AI initiatives with the district’s enterprise technology ecosystem.
The Director works in close alignment with the Chief Technology Officer, who serves as the final decision\-maker on district\-wide AI strategy, priorities, and investments. This role is responsible for driving execution, coordination, and implementation of AI initiatives, ensuring that work progresses efficiently, stakeholders remain aligned, and initiatives are delivered with fidelity to the district’s vision and standards.
Responsibilities:
AI Strategy, Instructional Enablement \& Scaled Implementation
- In collaboration with the Chief Technology Officer, define and lead the district\-wide strategy for AI adoption across instruction and operations
- Identify and prioritize high\-impact AI use cases, with a focus on instructional impact and educator effectiveness
- Design and oversee pilot initiatives, establishing clear success metrics and evaluation frameworks
Drive a “pilot evaluate* scale” model to accelerate adoption of effective solutions
- Partner with academic leadership to integrate AI into teaching and learning practices and support student outcomes
Governance, Policy \& Responsible Use
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- Establish and oversee policies and guidelines for responsible AI use, including:
- + student data privacy and protection
+ ethical and equitable use
+ acceptable use and guardrails
- Partner with cybersecurity, legal, and data teams to ensure compliance with district policies and regulations
- Define and manage processes for evaluating, approving, and monitoring AI tools and use cases
- Develop and implement staff AI Literacy training in collaboration with Digital Learning Team
- Partner closely with the Chief Technology Officer to translate strategic direction into actionable plans, ensuring alignment between vision, implementation, and outcomes across all AI initiatives.
Vendor Strategy, Tool Alignment \& Enterprise Integration
- Lead evaluation and selection of AI tools and vendors in collaboration with procurement, technology, and the AI Steering committee
- Establish and maintain an approved portfolio of AI solutions aligned with enterprise systems and architecture
- Prevent duplication and tool sprawl by enforcing alignment with district standards
- Monitor vendor performance and ensure solutions meet district requirements for privacy, security, and scalability
Cross\-Functional Leadership, Communication \& Continuous Improvement
- Establish and lead a cross\-functional AI Center of Excellence, including stakeholders from Instruction, Technology, Data \& Accountability, Family \& Community Engagement, and Operations
- Coordinate AI\-related efforts across departments to ensure alignment and effective execution
- Serve as the central point of communication for AI initiatives across the district
- Develop guidance, resources, and communication to support staff use of AI tools
- Stay current with emerging technologies and continuously refine strategy based on results and feedback
Qualifications \- Required:
- Experience with AI tools or digital innovation initiatives
- Bachelor’s degree in education, technology, public administration, or related field
- 5–7\+ years of experience leading for innovation; cross\-functional initiatives, programs, or technology transformations
- Foundational understanding of artificial intelligence and machine learning concepts, including large language models (LLMs), generative AI, and common use cases in education and operations
- Familiarity with enterprise technology environments, including system integrations, data platforms, and identity/access considerations
- Ability to evaluate AI tools and platforms for usability, scalability, data privacy, and security implications
- Understanding of data governance, privacy, and responsible use considerations related to AI systems
- Ability to translate technical concepts into clear, actionable guidance for non\-technical stakeholders
- Demonstrated experience driving organizational change and adoption of new tools or practices
- Strong leadership, communication, and stakeholder management skills
- Ability to operate effectively in complex, multi\-stakeholder environments
- Strong communication, organizational, and problem\-solving skills
- Current authorization to work in the United States
- An understanding of and commitment to gain greater understanding of what is necessary for an urban school system to enjoy continuous improvement in an increasingly complex and competitive environment.
- A deeply held and unyielding belief in the overarching mission of public education.
Qualifications \- Preferred:
- Experience in K\-12 education and/or public sector organizations
- Familiarity with data privacy regulations (e.g., FERPA) and responsible technology use
- Experience with vendor evaluation, procurement, and technology governance
- Experience supporting change management or implementation initiatives
- Familiarity with Boston and Boston Public Schools
UNION/MANAGERIAL/RESIDENCY REQUIRED: Managerial
Terms: D61 ($126,976\)
*The Boston Public Schools, in accordance with its nondiscrimination policies, does not discriminate on the basis of race, color, age, criminal record, physical or mental disability, pregnancy or pregnancy\-related conditions, homelessness, sex/gender, gender identity, religion, national origin, ancestry, sexual orientation, genetics, natural or protective hairstyle, military status, immigration status, English language proficiency, or any other factor prohibited by law in its programs and activities. BPS does not tolerate any form of retaliation, or bias\-based intimidation, threat or harassment that demeans individuals’ dignity or interferes with their ability to work or learn. If you require an accommodation pursuant to the ADA for the application process, please contact the Accommodations Unit at [email protected].*
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 Boston Public 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.
Boston Public Schools AI Hiring
Boston Public Schools has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $215,350 across 442 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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