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About This Role
WHO WE ARE
Edmentum is a dynamic educator and student-focused company dedicated to tech-enabled learning solutions. Our goal is to ensure that all students have access to flexible learning environments and educators have the tools they need to support their students. We are on a mission to create innovative, proven learning technology, partnering with educators to ignite student potential. We are a Remote First organization with a strong commitment to excellence, innovation, and customer satisfaction.
What You'll Do
AI is changing what educational technology can do for students, and the question of whether it actually works for learners is one the whole field is still figuring out. At Edmentum, we think that question deserves real evidence. As Manager, Applied AI Research, you will lead the team that produces it. You will design the studies that test whether our AI-enabled features are reliable, valid, and fair, and you will carry those findings into the rooms where product and policy decisions get made. Your work will shape both what we build and how the broader education community understands responsible AI in the classroom.
In this role you will:
- Lead a team of researchers producing rigorous, defensible evidence on the reliability, validity, and fairness of Edmentum's AI-enabled features.
- Design and oversee research and validation approaches for automated scoring, algorithmic decision support, and emerging AI capabilities.
- Represent Edmentum in academic, industry, standards-body, customer, and policy-facing forums, and build our external credibility on responsible AI.
- Partner with Product Management, Technology, Learning and Assessment Design, and Efficacy Research to shape the questions that matter and deliver answers on the timelines that matter.
- Help set the AI research agenda alongside senior research leadership, then turn those priorities into a clear plan of work for your team.
- Hire, coach, and develop researchers, and build a team that grows as fast as the field does.
- Manage university and third-party research partnerships, and hold them to high standards for quality and research ethics.
Who You Are
- You have a doctorate in psychometrics, quantitative psychology, educational measurement, statistics, data science, AI evaluation, or a related field.
- You bring 8 or more years of experience in psychometric analysis, measurement science, AI research, data science, or related technical domains.
- You have 2 to 3 years of experience managing complex research projects and leading individuals or a small team, and you take real satisfaction in developing people.
- You have designed or overseen research and validation approaches for AI-enabled features, automated scoring, algorithmic decision support, or adjacent innovation areas.
- You know the AERA/NCME/APA Standards for Educational and Psychological Testing well, along with the regulatory and market context that governs assessment quality.
- You follow the AI policy landscape in education and understand how evidence expectations are shifting for customers, states, and accrediting bodies.
- You have managed university or third-party research partnerships and held them to high standards for quality and ethics.
- You have built an external presence through peer-reviewed publications, conference presentations, or standards-body participation.
- You have established documentation, quality standards, and validation processes that hold up as an organization grows.
- You can translate technical findings into clear guidance that product, technology, and commercial partners can act on.
- You set priorities well and keep concurrent research projects moving against competing deadlines.
- You are willing to take on evolving responsibilities based on business needs.
Job Application Deadline: October 30, 2026
At Edmentum, we are committed to pay transparency. The salary range provided reflects market research, the responsibilities of the role, and alignment with our compensation principles. Actual compensation will be determined based on multiple factors, including, but not limited to, relevant experience and skill sets. All compensation decisions are individualized and based on the specific circumstances of each candidate.
We also offer a comprehensive benefits package to support your overall well-being and work-life balance. This includes medical, dental, and vision insurance with various plan options, a 401(k) retirement plan with company matching, and a time away program, volunteer time away, and a winter office closure at the end of December. Additionally, we provide resources to promote wellness, ensuring you have the support you need both professionally and personally. Our goal is to provide a benefits package that helps you thrive in all aspects of life.
Edmentum is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, national origin, ancestry, sex, age, disability, status as a disabled, recently separated, Armed Forces service medal or other protected veteran ("covered veteran"), marital status, status regarding public assistance, sexual orientation or any other characteristic protected by applicable federal, state or local laws.
We are dedicated to meeting the needs of individuals with disabilities and to creating an environment that supports our employees' physical and mental health. If you are a qualified individual with a disability or a covered veteran and need a reasonable accommodation to complete any part of the application process, please contact [email protected].
As part of our hiring process, we may use AI-powered tools to support our HR team in reviewing, screening, and managing applications. These tools aim to improve efficiency, consistency, and fairness, but final decisions are made by our people. Applicants' personal information (e.g., resume, cover letter, qualifications, and application responses) may be processed by third-party AI tools for tasks like resume parsing, skills matching, candidate ranking, and interview scheduling.
Edmentum's notice regarding the collection of personal information from interested candidates is available here
Salary Context
This $145K-$155K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Edmentum, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 29% below the category median. Disclosed range: $145K to $155K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Edmentum AI Hiring
Edmentum has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bloomington, MN, US. Compensation range: $155K - $155K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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