Founding Music AI Engineer

David Joseph & Company
  • San Francisco, California
  • $175,000–$225,000 Per Year
9 days ago

Job Description

San Francisco, CA · On-site · Full-time
Compensation: $175K–$225K + equity

About the Company

Our client is an early-stage AI lab building foundation models in the consumer music space, with live products already in the hands of a global user base. It's a small, close-knit team pairing deep ML research talent with consumer product builders, and this is a genuine ground-floor opportunity where each early hire shapes both the product and the culture.

Founded 2024 · ~5 people · Industry: AI, Consumer, Education, EdTech

The Role

Our client is hiring a founding engineer to help build production-ready ML systems that power the next generation of music learning for a large and growing base of musicians. You'll work closely with the Head of AI, taking abstract ideas and turning them into rock-solid, shipped implementations. Prior music-AI experience is not expected — the team already has domain experts and is looking for exceptional general technical ability paired with a startup mindset.

What you'll be doing

  • Building and shipping ML models for audio and symbolic music understanding into production systems used by real musicians
  • Translating the research team's ideas into solid, working engineering implementations
  • Partnering with data, design, and product to integrate models into user-facing features
  • Curating, preprocessing, and building pipelines around large-scale music datasets
  • Contributing to model evaluation, optimization, and deployment for real-world latency and reliability

Tech stack: Python, PyTorch / JAX, large-scale ML systems

Requirements

  • Zero to four years of experience in ML engineering, infrastructure, or a comparably demanding technical role
  • Time at a company known for a high technical bar — for example, big-tech infrastructure, quant, or a fast-scaling startup
  • Strong software engineering and hands-on coding ability
  • A track record of building and training ML models, whether in production systems or through substantial hands-on projects
  • A bachelor's or master's in computer science, electrical engineering, or a closely related technical field
  • Working proficiency with modern ML frameworks such as PyTorch or JAX
  • The ability to take research concepts and turn them into robust, shippable code
  • A collaborative working style that meshes well with researchers and fellow engineers
  • Based in San Francisco (or ready to relocate) and comfortable working in person for the majority of the week

Nice to Haves

  • Prior experience at a startup or other early-stage company
  • Exposure to audio, sequence modeling, or generative model domains
  • Experience building data pipelines or working with large-scale datasets

Why Join

  • A founding engineering seat at a pre-Series A AI lab, with meaningful early-stage equity
  • The chance to work directly alongside PhD researchers and product experts on genuinely novel consumer AI
  • Real influence over both the product and the team's culture from day one
  • Competitive compensation: $175K–$225K plus significant early equity

Details

  • Location — San Francisco, CA
  • Work policy — On-site; in person the majority of the week
  • Compensation — $175K–$225K + equity
  • Visa sponsorship — Open to visa transfers (e.g. OPT, H-1B transfers)
  • Employment type — Full-time

Numbers & Facts

LocationSan Francisco, California
Salary$175,000–$225,000 Per Year

Skills

  • Artificial Intelligence (AI)unmatched
  • Computer Scienceunmatched
  • Data Managementunmatched
  • Data Setsunmatched
  • Electrical Engineeringunmatched
  • JAX (Java API for XML)unmatched
  • Large-Scale Systemsunmatched
  • Musicunmatched
  • Power Generationunmatched
  • Product Designunmatched
  • Production Systemsunmatched
  • Python Programming/Scripting Languageunmatched
  • Research Skillsunmatched
  • Software Engineeringunmatched
  • Startupunmatched
  • Team Playerunmatched
  • Technical Recruitingunmatched

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