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Music Metadata Remote Jobs (NOW HIRING)

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Music Metadata Remote information

What is a music metadata remote?

A Music Metadata Remote job involves working from a remote location to organize, edit, and manage the descriptive information (metadata) associated with songs, albums, and artists in digital music libraries. This metadata includes details like track titles, artist names, genres, release dates, and album artwork. Accurate metadata is essential for music discovery, digital distribution, and royalty tracking. People in this role typically work for music streaming services, record labels, or metadata companies, ensuring that music databases are comprehensive and up to date.

What are the key skills and qualifications needed to thrive as a music metadata remote, and why are they important?

To thrive as a Music Metadata Specialist, you need a strong understanding of music cataloging, attention to detail, and familiarity with music genres and industry standards, often supported by a relevant degree or experience in music or library science. Proficiency with metadata management systems, digital asset management tools, and spreadsheets (like Excel) is typically required. Excellent organizational skills, communication, and the ability to work independently are vital soft skills for excelling in a remote environment. These competencies ensure accurate and consistent music data, which is crucial for discoverability, licensing, and user experience in digital music platforms.

What are some common challenges faced by remote music metadata specialists, and how can they be effectively managed?

Remote music metadata specialists often encounter challenges such as inconsistent data sources, varying metadata standards across platforms, and the need for precise attention to detail. Effective communication with team members and content providers is crucial, as is staying updated on industry metadata guidelines. Utilizing collaborative tools and maintaining organized workflows can help address discrepancies and ensure accuracy, ultimately supporting seamless music discovery and distribution.

What is the difference between Music Metadata Remote vs Music Data Entry Specialist?

AspectMusic Metadata RemoteMusic Data Entry Specialist
Required CredentialsKnowledge of music industry standards, basic digital literacyBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hours, digital platformsRemote or office-based, data-focused tasks
Employer & Industry UsageMusic labels, streaming services, digital distributorsMusic publishers, record labels, data companies
Search & Comparison IntentUnderstanding roles in music metadata managementData entry tasks in music industry

Music Metadata Remote involves managing and updating detailed information about music tracks, often requiring familiarity with industry standards and digital tools. Music Data Entry Specialist focuses on inputting music-related data into systems, emphasizing accuracy and speed. Both roles are remote-friendly and essential in the music industry, but Music Metadata Remote typically requires a broader understanding of music cataloging and metadata standards.

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Infographic showing various Music Metadata Remote job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 49% Full Time, 46% Part Time, 1% Temporary, and 2% Contract. Highlights an 94% Physical, and 6% Remote job distribution.

AI Video Generation Architect

Washington, DC โ€ข Remote

$170K - $200K/yr

Full-time

Re-posted 20 days ago


Job description

Start Date: ASAP

Role Type: Full-Time, Salaried

Background: Software development

Location: Remote, Flexible (USA based)

Salary: $170,000-$200,000 per year, plus benefits


Who We Are:

The Modern Classrooms Project (MCP) is a 501(c)(3) nonprofit organization that empowers educators to build classrooms that respond to every student’s needs.  Founded by two award-winning teachers, we lead a movement of educators in implementing a self-paced, mastery-based instructional model that leverages technology to foster human connection, authentic learning, and social-emotional growth.

To date, we have reached over 100,000+ teachers through our free online course in 150+ countries. We are an ambitious, idealistic team led by former classroom teachers, and we are passionate about what we do.
 

Job Description - Why we need you!  

Effective instructional videos make high-quality instruction accessible to all learners, regardless of experience or background. Every day, in classrooms around the world, Modern Classroom educators replace live lectures with instructional videos so that students can learn at their own paces, in school and/or at home. Good videos enhance learning — and they are time consuming to produce. A single high-quality lesson video can take hours to plan, record, edit, and caption.

We need an experienced, hands-on, AI-native engineer to build a brand new, state-of-the-art generative pipeline that turns specifications into high-quality instructional videos — complete with animations, synchronized AI narration, captions, and automated ground-truth quality verification. You will own the video render path end to end, from the canonical specification to the final rendered output, creating intuitive, powerful tools that will directly support educators and students every day.
 

Key Responsibilities
As our AI Video Generation Architect, you will be a senior individual contributor on our Engineering Team, reporting to the Head of Engineering and collaborating closely with the Chief Innovation Officer to ship features that make a real difference for students and educators.

You’ll be joining a small and growing team of talented software engineers working together to solve the problems teachers and students face every day. We’re building a world where every student can succeed, and we need you to help us make that happen.

You will:

  • Architect the video generation pipeline end to end. Design the gen-AI pipeline that transforms lesson specifications into storyboards, scene graphs, scripts, and production plans. Every stage emits deterministic lessons-as-code and structured intermediate artifacts — scene specs, asset manifests, timing maps — that can be inspected, versioned, cached, diffed, and selectively re-rendered.
     
  • Ship multiple substantial features per week. This is a minimum velocity bar, not an exaggeration. You will leverage AI and agentic coding to build incredible software, very, very quickly.
     
  • Build the multi-agent production workflow. Develop agentic orchestration (LangGraph or equivalent) in which an orchestrator delegates to specialist agents: pedagogy analyst, instructional scriptwriter, slide designer, animator, narrator, and a panel of graders, evaluators, and LLM judges, with structured outputs and human-in-the-loop labeling and fine-tuning.
     
  • Engineer the video generation pipeline. Build brand-consistent, design-system-driven video generation from structured content: layout engines and templates, LaTeX/KaTeX mathematical typesetting, programmatic diagrams and charts, and text-faithful image generation for illustrations with automated readability checks. Design programmatic motion to support worked examples with narration: kinetic typography, transitions, animated number lines and area models. Run parallelized rendering with generative video models (e.g. Veo / Kling / Seedance). Narration with TTS (e.g. ElevenLabs v3 / Gemini-TTS) audio tags and SSML, pronunciation lexicons for mathematical vocabulary, consistent voice identities across a course, multi-voice dialogue, multilingual narration, and open license music embeds.
     
  • Build the ground-truth quality system. Construct golden datasets of spec-to-video pairs annotated by educators. Implement rubric-based scoring with calibrated LLM- and VLM-as-judge evaluators: frame-level visual fidelity, verification of on-screen mathematics, A/V sync validation, pedagogical fidelity checks against the source spec's learning objectives, reading-level analysis, and K-12 content safety screens. Symbolically verify every worked example with a computable ground truth verification system — if the video teaches 3/4 + 1/8, a machine learning model should independently confirm the answer before any student sees it.
     
  • Architect resilient, high-scale media infrastructure. Design and scale the distributed backend across Python and TypeScript that carries the pipeline: render queues and job orchestration, transcoding and streaming (HLS), and provenance-aware metadata for AI-generated media. Own the systems design and ensure our foundational architecture is ready to scale.
     
  • Raise the bar for the team. Review the work of teammates and contractors. Collaborate with teammates on architecture and implementation reviews. Write PR comments, design docs, and agent skills that make the next person faster.
     

You should apply if:

  • You are AI-native. You are an expert in continuous multi-session development with Claude Code and/or OpenAI Codex. You are an expert at prompt engineering and context engineering. You write Agent Skills the way other engineers write unit tests. You practice Spec-Driven Development (GitHub Spec Kit or equivalent) as part of your normal workflow.
     
  • You have built real backend AI orchestration layers that run when you're not watching. You think in graphs — shared state flowing through nodes, conditional edges, interrupts, and circuit breakers. You have shipped non-trivial agentic pipelines using LangGraph, Python, and TypeScript, or equivalent. You treat durable execution, structured outputs, human-in-the-loop checkpoints, and provider-agnostic model routing as baseline design constraints. You have built evaluation harnesses, annotated datasets, and versioned prompt chains as first-class artifacts.
     
  • You are a programmatic media craftsperson. You have deep experience with a programmatic animation framework (e.g. Manim, Remotion / Motion Canvas) and strong FFmpeg fundamentals: codecs, containers, color, audio streams, muxing. You understand TTS model trade-offs, expressive direction with audio tags and SSML, pronunciation lexicons, forced alignment and word-level timestamps, and loudness standards. You can hear when the pacing is wrong for a twelve-year-old learner, and you fix it in the pipeline, not the waveform.
     
  • You treat quality as a measurable system. You build golden datasets and calibrated judges before you scale generation. You combine deterministic checks (schemas, layout constraints, symbolic math verification, A/V sync) with LLM- and VLM-as-judge evaluation validated against human labels. You catch the subtly wrong diagram, the mispronounced denominator, the worked example that's off by one — and the same eye applies to agent-generated code, which is plausible but not always right. You do not ship what you cannot measure.
     
  • You are self-directed. You thrive in small, high-autonomy teams and startups where the surface area is broad and the context shifts constantly. You write clearly. You own a problem end-to-end without waiting for a ticket to tell you what to do next.
     
  • You love to learn. You're actively leveraging the latest developments in AI and applying them to enhance both your own and others' work. You're also motivated by MCP's mission and vision, and eager to build teacher- and student-facing products.
     
  • You want to shape the world. You're motivated to be part of something larger than yourself. You believe that the highest value of your talent is using it to empower others. You're ready to make a real difference in educators' and young people's lives.
     

It would also be helpful if:

  • You have experience building edtech products.