Interested in this AI/ML Engineer role at Imagine Learning?
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About This Role
Great Purpose. Great People. Great Opportunities. At Imagine Learning, we Empower Potential \- in students, educators, and each other. As the leading provider of digital\-first K–12 curriculum solutions, we’re committed to igniting learning breakthroughs that spark curiosity, creativity, and confidence. Every day, our team designs innovative tools that help educators personalize instruction and engage students in meaningful ways. We’re shaping the future of learning through our Curriculum\-Informed AI™ approach \- technology that understands instructional context, respects educator judgment, and transforms how teachers personalize learning at scale to unlock each student’s potential. Together, we’re reimagining what’s possible and transforming education.
That same spirit of innovation and purpose drives our team culture. At Imagine Learning, you’ll find opportunities for meaningful work, continuous growth, and connection with passionate colleagues who care deeply about making a difference. We celebrate collaboration, embrace change, and believe in lifelong learning \- for our students and ourselves.
As a remote\-friendly company, Imagine Learning offers flexible work arrangements across the U.S. and internationally. Most of our U.S.\-based employees work from home or on a hybrid schedule at one of our office locations in Tempe, AZ (HQ), Austin, TX, Petaluma, CA, Rock Rapids, IA, or Bloomington, MN. Imagine Your Impact.
The AI Workflow \& Automation Architect reports to the AI Workflow \& Automation Manager and leads the design and execution of large\-scale content development and transformation initiatives, projects that take existing curriculum and systematically convert, translate, localize, or restructure it at a scale that manual processes cannot reach. Spanish translation of K–12 curriculum is the anchor use case for this role, with adjacent transformations (accessibility remediation, format migrations, grade\-band adaptations, additional languages) on the near\-term horizon. This role is both architectural and execution\-focused: scoping multi\-month transformation programs, building the AI\-assisted pipelines that power them, partnering with curriculum and localization SMEs to define quality bars, and operationalizing repeatable systems that successive projects can inherit rather than rebuild.
For more information on what it’s like to work at Imagine Learning, including our culture, benefits, and products, visit usHERE.
To learn more about a typical applicant journey at Imagine Learning, clickHERE*.*Position Information: This is a regular, full\-time position, reporting to the AI Workflow \& Automation Manager. Compensation: Base pay is anticipated to be between $83,335\.00 and $90,000\.00 per year. Eligible employees may also receive incentive/commission/annual bonus pay based on individual and/or company performance. Compensation may vary based on factors such as, but not limited to, individual skills, experience, training, education/certifications, geographic location, internal equity, and local market conditions. Location: In this US\-based position your location will be remote.
Travel: You can also expect up to approximately 10% travel, so be sure you have a valid driver’s license and automobile insurance and a clean driving record for at least the preceding 39 months.
Benefits: Imagine Learning provides a comprehensive benefits program to eligible employees, including:* Multiple health, dental, and vision plans, including medical plans with zero employee premiums
- 401k plan with a company match
- 16 paid holidays, which include 2 floating holidays and a winter shutdown from Christmas Eve through New Year’s Day
- Paid Time Off
- Comprehensive maternity and fertility/family building benefits
- Paid bonding leave when a new child joins your family
- Access to on\-demand mental health resources
- Life and short and long\-term disability insurance
- Pre\-tax savings plans
- Paid volunteer time off
- A wide variety of professional development programs, including tuition reimbursement
- Work from home opportunities that foster work/life balance
Envision Your Experience.
In this role you’ll have the opportunity to:* Program Architecture \- Scope and structure large\-scale transformation initiatives end\-to\-end: define the unit of work, sequence the pipeline stages, identify where human review is non\-negotiable versus where AI can run unattended, and produce the project plan that turns a multi\-thousand\-asset corpus into a tractable program. Spanish translation of curriculum is the immediate focus; the architecture must generalize to future transformations.
- Pipeline Development \- Build the AI\-assisted workflows that power transformation at scale using Python, LLM APIs, translation services, and workflow platforms (StackAI and AWS Flows). Engineer the connective tissue between source content systems, transformation steps, QA layers, and target destinations. Design pipelines that are observable, resumable, and produce auditable outputs.
- Quality Systems \- Partner with curriculum, localization, and content SMEs to translate tacit quality standards into explicit, measurable criteria — glossaries, style guides, evaluation rubrics, sample\-based QA protocols. Build the tooling that surfaces failure patterns early so human reviewers spend their time on judgment calls, not catching basic errors.
- Evaluation \& Tool Selection \- Pilot and evaluate translation APIs, LLM\-based translation and adaptation approaches, translation memory systems, and adjacent tooling for cost, quality, compliance, and integration fit. Make defensible recommendations on build\-versus\-buy and document the reasoning so decisions are reusable.
- Enablement \& Training \- Serve as the AI liaison to curriculum, localization, and content teams. Run trainings, document usage patterns, and create the prompt libraries, configuration templates, and runbooks that let partner teams operate transformation pipelines confidently. Translate technical concepts for non\-technical stakeholders.
- Operationalization \- Partner with the AI Workflow \& Automation Manager to productionize pilots into supported internal solutions. Document architecture, endpoints, configuration, and troubleshooting; contribute to the central catalog of approved tools, workflows, and patterns so each transformation project builds on the last.
- Collaboration \- Work closely with curriculum experts, localization specialists, and engineering/ops partners. Focus on building enablement infrastructure and automation systems, not on producing the transformed content directly.
- Other duties as required.
Share Your Expertise.
Experience, education, and qualifications essential for success in this role, include:* Bachelor's degree in Computer Science, Linguistics, Product Management, Education, Business, or a related field, and at least 3 years of relevant experience, including at least 2 years working on SaaS, AI/ML, localization, or large\-scale content transformation projects; or an acceptable combination of education and experience.
- Demonstrated experience scoping and delivering large\-scale content transformation, localization, or migration projects.
- Working knowledge of localization and translation workflows: translation memory, glossary and termbase management, style guides, and linguistic QA.
- Scripting proficiency in Python; comfort working with APIs, webhooks, and workflow automation platforms (StackAI, AWS Flows, PowerAutomate or comparable) is preferred.
- Demonstrated ability to evaluate AI tools and translate fuzzy quality requirements into measurable evaluation criteria.
- Excellent collaboration, documentation, and communication skills; ability to translate technical concepts for non\-technical partners and surface tacit knowledge from SMEs.
- Strong problem\-solving skills, intellectual curiosity, and a habit of pressure\-testing your own reasoning.
- Preferred: K–12 EdTech experience.
- Preferred: Spanish proficiency or bilingual fluency, particularly with educational content.
- Preferred: Experience with translation memory or TMS platforms.
- Preferred: Experience designing LLM evaluation harnesses or human\-in\-the\-loop review systems.
- Preferred: Familiarity with K–12 curriculum content structures and learning standards (e.g., CCSS, TEKS).
- Innovative mindset and the ability to learn new technologies quickly.
- Highly proactive, organized, and comfortable operating in fast\-paced, evolving environments.
- Deep passion for educational equity and innovation, with a strong interest in transforming learning through AI.
- Willingness to travel up to 10% to engage with stakeholders and internal teams as needed.
At Imagine Learning, we believe our work is strongest when people feel respected, supported, and encouraged to contribute their unique perspectives. We strive to create a welcoming workplace where employees can learn from one another, grow together, and build educational experiences that reflect the world of our learners.
Imagine Learning is an Equal Opportunity Employer committed to providing equal employment and advancement opportunities to qualified individuals. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, sex (including pregnancy, childbirth, lactation, or related medical conditions), gender identity or expression, transgender status (including whether or not you are transitioning or have transitioned), sexual orientation, marital status, religion (including religion dress and grooming practices), age 40 and over, physical or mental disability, medical condition, genetic information (including results of genetic testing and characteristics), veteran and/or military status, or service in the military, and any other basis or status protected under applicable federal, state, or local laws. *To all recruitment agencies:* *Imagine Learning does not accept agency resumes. Please do not submit candidates for consideration via our online application system, to Imagine Learning employees, or to any other organization location. Imagine Learning is not responsible for any fees related to unsolicited resumes.*
Salary Context
This $83K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Imagine Learning, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($86K) sits 52% below the category median. Disclosed range: $83K to $90K.
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.
Imagine Learning AI Hiring
Imagine Learning has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $90K - $90K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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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