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Office: Office of Teaching and Learning
Date Posted: 8/4/2026
Salary Range: 1\-5 / $98,423 \- $111,058
NTE Date: N/A
Position Overview
The mission of the Office of Teaching and Learning (OTL) is to provide educators with curricular resources, academic programs, and aligned professional development to ensure rigorous and joyful learning experiences for every student. OTL team members support school\-based staff in implementing DCPS's existing academic programs while simultaneously working to rethink and redesign school programming, academic and curricular resources, and educator professional development.
Divisions/Teams
- The Content and Curriculum division sets the vision for equity and excellence for each content area in DCPS schools by researching, creating, and curating curricular materials and providing aligned professional development experiences to ensure that all students have access to rigorous and joyful learning experiences every day in every content area. The division also supports schools and develops curriculum and programming in arts, health, physical education, library programs, STEM, global programs, advanced and enriched instruction, and curriculum and assessment innovation.
- The Early Childhood Education division supports schools and families in providing PreK students with comprehensive learning experiences that foster confidence and independence. Approximately 6,000 children ages three to five attend early childhood classrooms in 78 elementary schools across the district, including several schools that offer Head Start programming. The division also manages Early Stages programming which works exclusively with children ages 2 years 8 months through 5 years 10 months and is responsible for meeting the District’s obligation under the Individual with Disabilities Education Act (IDEA) Part B (619\) to develop a comprehensive child find system to identify and locate all preschool\-age children in the District who have a disability. The division also manages the implementation of DCPS' Head Start grant.
- The Language Acquisition division welcomes families of linguistically and culturally diverse backgrounds to DCPS, leads the process of identifying Multilingual Learner students, and supports schools in developing programs that supports English language acquisition and academic growth. This includes support during the academic day and as part of ELSAP, which is a targeted summer program for ML students. The LAD also manages DCPS compliance with the DC Language Access Act, providing translation and interpretation resources to school and central teams.
- The Professional Learning and Educational Technology division sets a vision for professional development for DCPS as well as creates systems, tools, and structures to increase the efficacy of professional learning initiatives. This work includes LEAP, cluster\-based PD, district PD and other learning opportunities. The team works across all DCPS offices to increase the cohesion of professional development and broad sharing of knowledge, while exploring new avenues and opportunities for meaningful development and learning for our educators. The team also leads efforts to enhance alignment and cohesion of professional learning initiatives and the integration of educational technology.
- The Specialized Instruction division works to reduce the opportunity gap between students with IEPs and their non\-disabled peers; and increases equity and excellence in schools through specific academic programming, related services, professional learning, school leadership development, family engagement, dispute resolution, and community partnerships.
The Professional Learning and Educational Technology division sets a vision for professional development for DCPS as well as creates systems, tools, and structures to increase the efficacy of professional learning initiatives. This work includes LEAP, cluster\-based PD, district PD and other learning opportunities. The team works across all DCPS offices to increase the cohesion of professional development and broad sharing of knowledge, while exploring new avenues and opportunities for meaningful development and learning for our educators. The team also leads efforts to enhance alignment and cohesion of professional learning initiatives and the integration of educational technology.
The Manager, Educational Technology (AI) leads implementation of district priorities related to instructional technology, AI literacy, and digital learning systems. This role supports schools and central teams in integrating technology to improve teaching and learning, while ensuring equitable, safe, and effective use of digital and AI tools.
This position has been designated as Protection Sensitive. Pursuant to section 410 of Chapter 4 of the D.C. Personnel Regulations; in addition to the general suitability screening, individuals applying for or occupying protection sensitive positions are subject to the following checks and tests: Criminal background check; Sex Offender Registry check; Pre\-employment drug and alcohol test; Traffic record check (as applicable); Reasonable suspicion drug and alcohol test; and Post\-accident or incident drug and alcohol test.
The Manager, Educational Technology (AI) will report to the Director, Integrated Learning.
Essential Duties and Responsibilities
*The below statements are intended to describe the general nature and scope of work being performed by this position. This is not a complete listing of all responsibilities, duties, and/or skills required. Other duties may be assigned.*
- Partner with school and central services leaders to integrate technology and AI into instruction aligned to district goals.
- Design and deliver professional learning (in\-person, virtual, and asynchronous); develop and manage Canvas\-based training and resources.
- Develop and curate curriculum\-aligned digital and AI\-enabled instructional resources in collaboration with academic teams.
- Lead AI literacy initiatives, including training, guidance, and resource development for staff, students, and families.
- Coach educators on effective technology integration and instructional best practices.
- Manage projects and initiatives, including timelines, communications, and stakeholder engagement.
- Support platform integration, data use, and digital tool effectiveness across systems.
- Monitor emerging trends in educational technology and AI to inform district strategy.
Qualifications
- Bachelor’s degree and three to five years of relevant experience.
- Experience supporting instructional technology integration and adult professional learning.
- Master’s degree in education, instructional technology, or related field preferred.
- Teaching experience and experience with Canvas LMS, Microsoft 365, or similar platforms.
- Knowledge of technology standards and adult learning theory.
- Knowledge of AI literacy and instructional applications of AI tools.
- Ability to design training, resources, and professional learning experiences.
- Understanding of data privacy and compliance requirements (FERPA, COPPA, CIPA).
- Strong collaboration, communication, and project management skills.
DCPS Values
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- STUDENTS FIRST: We recognize students as whole children and put their needs first in everything we do.
- COURAGE: We have the audacity to learn from our successes and failures, to try new things, and to lead the nation as a proof point of PK\-12 success.
- EQUITY: We work proactively to eliminate opportunity gaps by interrupting institutional bias and investing in effective strategies to ensure every student succeeds.
- EXCELLENCE: We work with integrity and hold ourselves accountable for exemplary outcomes, service, and interactions.
- TEAMWORK: We recognize that our greatest asset is our collective vision and ability to work collaboratively and authentically.
- JOY: We enjoy our collective work and will enthusiastically celebrate our success and each other.
We are an equal opportunity employer and are committed to creating an inclusive, accessible workplace. We welcome and encourage applications from individuals with disabilities. Accommodation and/or application assistance is available upon request at all stages of the application and employment process. To request accommodation, please contact dcps.eeo\-ada@k12\.dc.gov.
Salary Context
This $98K-$111K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At District of Columbia 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($104K) sits 51% below the category median. Disclosed range: $98K to $111K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
District of Columbia Public Schools AI Hiring
District of Columbia Public Schools has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Washington, DC, US. Compensation range: $111K - $111K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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