Job Description
Job DescriptionAbout the Role
This is a founding engineering role at an early-stage EdTech startup building foundation models for music education, working directly with the Head of AI. You'll own the full arc from research to production, shipping ML systems that serve a large and growing community of musicians. The role carries meaningful equity upside and significant influence over the technical direction of the product.
What You'll Do
- Implement and ship ML models for audio and symbolic music understanding into production systems used by real musicians.
- Translate research ideas from the AI team into solid, working engineering implementations.
- Collaborate with data, design, and product teams to integrate models into user-facing features.
- Curate, preprocess, and build data pipelines for large-scale music datasets.
- Contribute to model evaluation, optimization, and deployment with a focus on real-world latency and reliability.
What We're Looking For
- 3 or more years of experience as a software engineer or ML engineer building and shipping production systems.
- Strong proficiency in Python and ML frameworks such as PyTorch or JAX.
- Demonstrated ability to translate ML research into robust, production-ready implementations.
- Experience building and training ML models in real production environments or substantial projects.
- Background from a technically demanding environment, such as large-scale infrastructure, quantitative finance, or a high-growth startup.
- BS or MS in Computer Science, Electrical Engineering, or a closely related technical field.
- Experience in audio processing, sequence modeling, or generative model domains is a plus.
- Experience building data pipelines and working with large-scale datasets is a plus.
- Authorized to work in the United States without visa sponsorship.
Compensation & Benefits
Base salary: $175,000 to $225,000 USD annually. This is a founding role with significant equity upside. Visa sponsorship is not available.
Location
On-site in San Francisco, California. Candidates should be based in San Francisco and able to work in-person the majority of the week.