AI/ML engineer

Melodex Studio, Inc.; United States; Remote; USD 120,000 - 170,000; Full time

Benefits offered by this job

Home setup funded

Completely remote work

Job summary

Melodex Studio, Inc. is hiring a machine learning engineer to build models that edit music, from notes to arrangement. You will train, evaluate, and ship models that handle structured music, turning producer requests into scoped changes.

The role is fully remote, requires hands-on Python and PyTorch, and experience training or fine-tuning models. You will own a project from start to finish and collaborate with the desktop team to land tools in the studio product.

Qualifications

• At least one year of ML engineering or research experience.
• Hands-on Python and PyTorch experience; trained or fine-tuned models.
• Ability to set up evaluation, analyze failures, and iterate.

Responsibilities

• Train, evaluate, and ship models that edit structured music.
• Turn producer requests into scoped, undoable musical changes.
• Collaborate with the desktop team to land tools in the product.

Skills

Python

Machine learning

Model fine-tuning

Evaluation pipelines

Remote work experience

Tools

PyTorch

Job description

Melodex keeps the song as a project. Tansen is the model layer that has to understand tracks, sections, MIDI, and a request like making the chorus bigger while leaving the vocal melody alone.

This role is for someone who wants to make models that edit music. You will work on training, evaluation, and inference, and on the path that turns a producer’s request into a scoped, undoable change inside the studio.

The interesting problems are musical. Keeping a melody intact, changing the energy of one section, and checking whether an edit still holds together two minutes later matter more to us than a prettier demo clip.

What you will do

You will train, evaluate, and ship models that work on structured music, from notes, rhythm, and harmony through arrangement and section-level edits. Copilot should turn a natural-language request into a musical plan that respects what the producer asked to keep.

Evaluation has to go further than whether something sounds fine. You will look at whether constraints were followed, whether the song still holds together, and whether the edit is actually usable. The data is real session history, including accepts, undos, tweaks, and the gap between a good loop and a finished arrangement.

You will work with the desktop team so the models land in the product as tools producers can use.

Requirements

We are looking for at least a year of machine learning engineering or research, with hands-on Python and a modern stack such as PyTorch. You should have trained or fine-tuned models yourself, and you should be able to set up evaluation, look at failures, and keep iterating. The team is small and fully remote, so you will own a problem from start to finish.

Good to have

Work in music information retrieval, symbolic MIDI, audio models, or sequence models is useful. So is experience with transformers, diffusion, or other generative models, and with production inference, latency, and shipping a model into a product. Music theory, composition, time in a DAW, or generation that has to follow constraints will help.

What we offer

Pay is competitive, and it follows the work. We also cover a home setup, so you can put together a proper desk, monitor, chair, and the basics of a working studio.

The roles are completely remote. There is no office to relocate to and no days-in-building requirement. The team is small, the product is a desktop studio producers already use, and you will own problems from one end to the other.