RTHYMS
Company:
https://www.linkedin.com/company/rthyms
Seniority: Executive
Industries: Online Audio and Video Media
Job details:
RTHYMS
Automated Music Label Services
Chief Digital Officer (CDO)
Focus: AI Systems, Data Science & Agentic Architecture
Location: Remote (Flexible) Type: Full-Time, Founding-Level Role Reports To: Founder / CEO
Company Overview
Rthyms is an automated music label services platform built for the modern music industry. The platform combines predictive analytics, intelligent artist matchmaking, influencer marketing automation, and real-time streaming intelligence into a single ecosystem -- serving independent artists, record labels, and marketing agencies. The product is built, in active development, and approaching market launch.
What makes Rthyms different from every other music-tech SaaS is the intelligence layer underneath it. Recommendations, matching, forecasting, discovery -- these are not features we bolt on. They are the product. And we need a leader who thinks the same way.
Position Summary
We are looking for a Chief Digital Officer to own the entire intelligence and data architecture of the Rthyms platform. This is not a generalist engineering role. This person will design and lead the AI systems, data science pipelines, and agentic workflows that power Rthyms' core value proposition.
You will define how the platform thinks -- how it matches artists to labels, how it discovers influencers, how it forecasts streaming performance, and how its autonomous agents act on data without waiting for a human to click a button. You will work directly with the Founder on product strategy, own the technical roadmap for all AI and data workstreams, and eventually hire and lead a small AI/data engineering team.
This is a founding-level role with significant equity and full technical autonomy over the intelligence layer.
Key Responsibilities
AI Strategy & Agentic Architecture
- Design the overall agentic architecture for Rthyms -- defining agent roles, orchestration patterns, memory systems, and tool-use frameworks
- Build multi-agent pipelines for autonomous workflows: influencer discovery, artist-label matchmaking, campaign optimization, and streaming trend detection
- Own the agent orchestration layer (LangGraph, CrewAI, AutoGen, or custom-built depending on your judgment)
- Define how agents interact with internal APIs, external data sources, and human-in-the-loop checkpoints
- Design context and memory persistence strategies for long-running agentic workflows (vector stores, graph memory, structured state management)
- Evaluate and integrate frontier models and open-source alternatives based on task fit, latency, and cost
Data Science & Predictive Intelligence
- Build and own the core predictive models: streaming performance forecasting, artist growth trajectory, matchmaking scoring, trend detection
- Design and manage feature engineering pipelines from Spotify API, Soundcharts, social scraping, and internal behavioral data
- Develop recommendation algorithms for artist-label matching and influencer identification
- Define model evaluation frameworks, offline metrics, and production monitoring for model drift and degradation
- Build and maintain a feature store and experiment tracking infrastructure (MLflow, Weights & Biases, or equivalent)
- Translate business questions from the Founder directly into modeled, measurable outcomes
Data Infrastructure & Pipelines
- Architect the data platform: ingestion, transformation, storage, and serving layers
- Design ETL/ELT pipelines from all data sources (Spotify API, Soundcharts, Puppeteer scraping, WebSocket streams, internal events)
- Select and manage the data warehouse or lakehouse (BigQuery, Snowflake, or equivalent) for analytical workloads
- Define data models that serve both real-time dashboards and batch ML training jobs
- Establish data quality, lineage, and observability standards from day one
- Build pipelines that are reproducible, versioned, and fail loudly (not silently)
AI-Powered Product Features
- Own the intelligence behind every "smart" feature on the platform: matching scores, campaign recommendations, streaming predictions, influencer rankings
- Build RAG systems and embedding-based search for artist discovery and content retrieval
- Design and maintain prompt engineering standards, LLM evaluation pipelines, and guardrails for production AI features
- Work with the engineering team on API contracts between the AI layer and the frontend/backend
- Define the product roadmap for AI feature expansion over 12-24 months
Platform Integration & Collaboration
- Collaborate with the engineering lead on architecture decisions where AI and product infrastructure intersect
- Ensure all AI systems comply with data privacy requirements and music industry standards (ISRC, DDEX, royalty data handling)
- Provide clear technical specifications for engineering implementation of AI features
- Contribute to the overall system architecture with a data-first perspective
Team Building (6-12 Months)
- Define and prioritize AI/data engineering hiring: ML engineers, data engineers, data scientists
- Recruit and onboard 2-3 AI/data specialists as the platform scales
- Establish best practices for model development, deployment, and monitoring
- Build a culture of experimentation -- fast iteration cycles, clear metrics, honest post-mortems
Required Qualifications
- 7+ years of professional experience in AI, machine learning, or data engineering roles
- 3+ years designing and shipping production ML systems or AI-powered products
- Strong proficiency in Python (the non-negotiable language for this role)
- Deep hands-on experience with LLM APIs (OpenAI, Anthropic, or equivalent) and prompt engineering in production
- Proven experience building agentic systems or multi-agent pipelines (not just prototypes -- systems that ran in production)
- Experience with vector databases and embedding-based retrieval (Pinecone, Weaviate, pgvector, Chroma)
- Strong data engineering background: pipeline design, transformation, orchestration (Dagster, Airflow, Prefect, or equivalent)
- Hands-on experience with cloud data warehouses (BigQuery, Snowflake, Redshift)
- Solid understanding of recommendation systems, ranking algorithms, and scoring models
- Comfortable working independently, making high-judgment architectural decisions with incomplete information
- Ability to communicate AI/data concepts to non-technical founders and stakeholders without dumbing it down
Preferred Qualifications
- Experience in music tech, creator economy platforms, or streaming data systems
- Familiarity with music data standards (ISRC, UPC, DDEX) and streaming platform APIs (Spotify, Apple Music, Soundcharts)
- Background in social data analysis, influencer scoring, or social media API integration
- Experience building real-time ML inference pipelines (low-latency, streaming predictions)
- Familiarity with LangGraph, CrewAI, AutoGen, or similar orchestration frameworks
- Experience with audio ML or audio feature extraction (tempo, energy, mood classification)
- Prior role as founding AI/data lead or first ML hire at a startup
- Understanding of the music industry -- label structures, royalty flows, artist development cycles
Tech Stack (AI & Data Layer)
Layer
Technologies
Agentic Orchestration
LangGraph / CrewAI / AutoGen / Custom
LLM APIs
OpenAI, Anthropic (Claude), open-source alternatives
Vector & Memory
pgvector, Pinecone, Chroma, or equivalent
Data Pipelines
Dagster / Prefect / Airflow
Data Warehouse
BigQuery / Snowflake (to be finalized)
ML Experimentation
MLflow / Weights & Biases
Data Ingestion
Spotify API, Soundcharts API, Puppeteer scraping
Languages
Python (primary), TypeScript (for API contracts)
Storage
PostgreSQL, S3-compatible object storage
Monitoring
Sentry + custom model monitoring
What You Will Own
To be concrete about scope: you will own the parts of Rthyms that think.
The artist matchmaking engine. The streaming forecast models. The influencer discovery and scoring pipeline. The autonomous campaign optimization agents. The real-time intelligence that surfaces insights to users before they know to ask for them.
The engineering team builds the platform. You build the brain.
Compensation & Structure
- Competitive salary commensurate with experience and company stage
- Significant equity in a founding-level role
- Direct collaboration with the Founder on product vision, business strategy, and go-to-market
- Full autonomy over AI/data architecture decisions
- Remote-first, async-friendly work environment
Ideal Candidate Profile
Someone who has built AI systems that actually ran in production and has the scar tissue to prove it. You've debugged agent loops that ran out of context, chased down data pipeline failures at 2am, and argued convincingly for a simpler model over a flashier one. You think in systems, not features.
You don't need to be a music industry insider, but you need genuine curiosity about the problem space. Independent artists navigating the industry deserve better tools, and the data to build those tools exists. Connecting the two is the job.
Screening Criteria
When evaluating candidates, prioritize:
- Shipped AI systems -- evidence of production ML or agentic systems, not just Kaggle notebooks or side projects
- Agentic architecture experience -- has designed multi-agent systems with real orchestration complexity
- Data engineering depth -- pipelines, warehouses, feature stores, not just model training
- LLM production experience -- prompt engineering, evaluation, guardrails, cost management in live systems
- Startup operating experience -- has owned technical scope in a small team with moving targets
- Communication -- can translate AI concepts into product decisions the Founder can act on
Interested? Reach out to us at RTHYMS.
EMAIL: gurjot@rthym.com
RTHYMS | Confidential
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