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Freelance AI Music Producer / SongwriterBlues, Soul, Country\-Soul \& AmericanaAbout Us
We are a U.S.\-based music and entertainment company developing and operating a growing portfolio of AI\-assisted virtual artists.
Our content has built a combined social media audience of more than 1 million followers and generates millions of music streams. We create emotionally driven music primarily for a mature U.S. audience, with a strong focus on listeners aged 40 and older.
Our main musical styles include:
- Blues
- Soul
- Country\-soul
- Americana
- Vintage\-inspired ballads
- Emotionally mature, story\-driven music
The identities and public accounts of our artists will be shared with selected candidates during the interview process.
Position Overview
We are looking for a creative, reliable, and detail\-oriented freelance AI Music Producer / Songwriter to help us develop original songs for our portfolio of virtual artists.
This is not simply a prompt\-writing position.
The selected producer should be able to understand each artist’s character, emotional identity, target audience, and musical direction. You will be expected to develop song concepts, create or refine lyrics, produce multiple musical versions, evaluate the results, and deliver polished songs that meet our creative standards.
We are especially interested in creators who understand emotionally mature storytelling and can produce songs that connect with American listeners aged 40 and older.
Responsibilities
- Develop original song concepts based on assigned artist identities, themes, and creative direction
- Write, edit, or refine English lyrics with clear and emotionally compelling storytelling
- Produce original songs using AI\-assisted music\-production tools
- Generate and evaluate multiple versions before selecting the strongest result
- Create memorable choruses, titles, hooks, and song structures
- Maintain consistency in each artist’s voice, genre, personality, and emotional identity
- Revise songs based on detailed creative feedback
- Organize lyrics, prompts, audio files, versions, and related production materials
- Collaborate with the creative director on song development and final selection
- Deliver completed work according to agreed schedules and quality standards
Preferred Qualifications
- Experience using AI music\-generation or AI\-assisted production platforms
- Strong understanding of blues, soul, country\-soul, Americana, or related genres
- Strong English lyric\-writing, editing, or songwriting ability
- Understanding of American songwriting structure and natural lyrical phrasing
- Ability to create emotionally mature, relatable, and memorable songs
- Good judgment when comparing and selecting from multiple generated versions
- Understanding of verses, pre\-choruses, choruses, bridges, hooks, pacing, and song dynamics
- Ability to follow established artist, character, and brand guidelines
- Strong communication, organization, and file\-management skills
- Ability to accept detailed feedback and complete revisions reliably
Experience creating music for mature American audiences will be considered a strong advantage.
Applicants who currently operate a music channel, artist page, YouTube channel, or music\-focused social media account are strongly encouraged to provide links.
Original songs, released tracks, demos, or a portfolio of AI\-assisted music will be considered a strong plus.
Tools and Resources
All AI tools, paid software subscriptions, and production resources required for assigned projects will be provided by the company.
The selected contractor will not be expected to purchase or maintain separate paid AI music\-production subscriptions for company projects.
Compensation
This freelance position includes:
- Guaranteed base compensation of $1,000
- Additional performance\-based compensation based on the commercial performance of songs created by the contractor
Songs that are approved and commercially released may qualify for additional compensation based on their streaming performance and the streaming revenue received by the company.
Base compensation is paid for completed and approved work. Performance\-based compensation is paid in addition to the base amount and is not a replacement for base compensation.
Detailed terms—including qualifying songs, attribution rules, revenue calculations, commission percentage, commission duration, reporting periods, and payment schedules—will be clearly defined in the written agreement.
Performance\-based compensation does not provide ownership, publishing, master, royalty, approval, or control rights unless expressly stated in the written agreement.
Ownership and Confidentiality
All approved music, lyrics, recordings, prompts, project files, character materials, and related creative assets produced under this engagement will be owned exclusively by the company under the terms of a written agreement.
The written agreement will include work\-made\-for\-hire provisions where legally applicable, together with an assignment of intellectual\-property rights to the company.
Selected candidates may be required to sign confidentiality and intellectual\-property agreements before receiving access to:
- Unreleased music
- Artist identities and public accounts
- Character and narrative information
- Internal performance and revenue data
- Proprietary production workflows
- Future release plans
Company materials, artist information, unreleased work, and completed deliverables may not be published, shared, reused, sold, or included in a personal portfolio without prior written permission.
Application Requirements
Please submit:
- A short introduction
- A summary of your relevant music\-production or songwriting experience
- Two to five examples of original songs you have created
- Links to any music channel, artist page, or music\-focused social media account you currently operate
- A brief description of the AI music tools you have used
- Your experience with blues, soul, country, country\-soul, or Americana music
- Your availability
- Your preferred base compensation structure
Applicants selected for the next stage may be invited to complete a short paid test project.
Engagement Details
- Freelance / Independent Contractor
- Remote
- Ongoing opportunities may be available based on quality, reliability, creative compatibility, and performance
Pay: From $1,000\.00 per month
Benefits:
- Flexible schedule
Work Location: Remote
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 Archive Room Lab, 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.
Archive Room Lab AI Hiring
Archive Room Lab has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 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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