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About Panorama
Panorama Education is a technology company building the leading platform for student insight and action in K\-12\. We help modern school systems understand what students need and take effective action. By bringing together academics, attendance, behavior, and engagement data with AI, we give educators the full picture of every student and the intelligence to decide what to do next. As a mission\-driven company supporting 15 million students in 2,000 districts nationwide, the work we do has a direct line to student outcomes every day. Join us and help shape what education looks like for the next generation.
About the Role
Panorama Education is seeking an AI Solutions Consultant, Special Education (1099\) to bring deep special education expertise to our work supporting educators and special education teams.
In this role, you will combine your knowledge of special education practice, documentation, and state and federal requirements with a curiosity about what AI can make possible for educators. You will help design, test, and refine ways our platform can be used to support complex special education workflows, including Individualized Education Programs (IEPs), evaluations, Functional Behavioral Assessments (FBAs), Behavior Intervention Plans (BIPs), specially designed instruction, and other special education processes.
We believe AI, when used responsibly, can reduce administrative burden, improve consistency and quality, and give special educators more time to focus on students. At the same time, AI should support, not replace, the expertise and professional judgment of educators and other qualified professionals. We are looking for someone who can bring both perspectives to this work.
Please note that this opportunity is for a 1099 contractor opportunity beginning with a 6\-month contract term, with the intention of extending the contract after that. Available project work ranges from 5\-20 hours a week depending on client needs and internal capacity.
If you are interested in full\-time employment with Panorama, please see currently open opportunities on our Careers page.
Requirements
As an AI Solutions Consultant, Special Education (1099\), you will:
- Serve as a special education subject matter expert, bringing strong knowledge of special education practice and state and federal requirements to the design of AI\-supported special education workflows.
- Design and refine AI\-supported solutions that use our platform features to support special education workflows such as IEP development, special education evaluations, FBAs, BIPs, and related processes.
- Learn how educators currently complete complex workflows, identify pain points and opportunities, and translate those needs into practical AI solutions.
- Work with district and state requirements, templates, protocols, and exemplars to ensure solutions reflect the context in which special educators work.
- Use generative AI to develop and iterate prompts, instructions, tool logic, and outputs.
- Test AI\-generated outputs for accuracy, completeness, usefulness, and alignment with high\-quality special education practice.
- Identify potential errors, omissions, inappropriate recommendations, or other risks and refine accordingly.
- Apply strong human judgment to determine where AI can meaningfully support educators and where professional expertise and decision\-making must remain central.
- Collaborate with Panorama team members to translate special education expertise into scalable tools, resources, and guidance.
- Provide clear feedback and recommendations that help Panorama continuously improve its AI\-powered solutions for special educators.
What We're Looking For
You bring deep special education expertise and are excited to apply that expertise in a new way. You don't need to be an AI engineer or software developer, but you should be comfortable experimenting with generative AI, learning how AI tools work, and using your professional judgment to make their outputs better.
Strong candidates will have:
- Significant professional experience in K–12 special education as a practitioner, leader, psychologist, diagnostician, consultant, or related role.
- Strong knowledge of federal special education requirements and processes, as well as an understanding of how state and local requirements shape special education practice, and a demonstrated commitment to actively staying current as laws, regulations, guidance, and requirements evolve.
- Deep familiarity with key special education documents and workflows, including IEPs, special education evaluations, FBAs, BIPs, and related documentation.
- The ability to recognize what high\-quality special education documentation and practice look like, and identify when something is inaccurate, incomplete, inappropriate, or potentially noncompliant.
- Experience synthesizing multiple sources of student information, such as assessment results, observations, progress\-monitoring data, educator input, family input, and prior documentation.
- Comfort using generative AI tools and an interest in using AI to solve meaningful problems for educators.
- A belief that responsibly designed AI can help special educators work more efficiently and effectively while keeping educators and qualified professionals in control of consequential decisions.
- Strong written communication, critical thinking, attention to detail, and professional judgment.
- The ability to work independently, navigate ambiguity, and iterate toward high\-quality solutions.
Particularly Valuable
We'd be especially excited about candidates who have:
- Experience working across multiple districts or states and navigating differences in special education requirements and practices.
- Experience designing or improving AI prompts, tools, workflows, or other technology\-enabled solutions.
- Experience reviewing special education documentation for quality or compliance.
- Experience supporting district\-level special education implementation, professional learning, or systems improvement.
- Experience translating complex special education requirements into clear guidance, templates, processes, or tools for educators.
What Success Looks Like
Your work will help Panorama create AI\-powered special education solutions that educators can trust and use.
Successful solutions will reduce repetitive administrative work while maintaining a high bar for quality; reflect relevant state, district, and professional requirements; synthesize information into useful, editable starting points; and keep educators and other qualified professionals firmly in control of review, interpretation, and final decisions.
Most importantly, the solutions you help design will give special educators more time and capacity to focus on what matters most: delivering high\-quality support to students.
Engagement Details
This is a 1099 consulting engagement with Panorama Education. The consultant will support defined projects and deliverables related to special education expertise and AI solution design, testing, and refinement. Specific projects, hours, timelines, and deliverables will be established based on project needs.
Panorama Education is dedicated to building a diverse and inclusive company because we serve students, educators and families from tremendously diverse backgrounds and identities across the country; we’ve seen how our product and impact are strengthened the more we reflect that diversity. In addition, we have found (and we believe the research) that diverse teams are higher\-performing, and we embrace the varied perspectives that our team members share with each other. As such, we are an Equal Opportunity Employer.
Benefits
This role is compensated at $90/hour, with most work scoped and paid using established project\-based rates. Contract terms typically begin at six months, with the intention to extend based on performance, availability, and ongoing demand.
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 Panorama Education, 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.
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.
Panorama Education AI Hiring
Panorama Education has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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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