We are seeking a founding ML engineer to build production-ready audio and symbolic music understanding systems for a global consumer music learning platform. The role involves translating research concepts into robust, shippable code, curating large-scale music datasets, and collaborating with data, design, and product teams. Candidates must have strong software engineering skills, hands-on ML experience, and a startup mindset.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
San Francisco, CA
- On-site
- Full-time Compensation: $175K–$225K + equity
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
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.
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- 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
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
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- 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
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- 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
- 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
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