Last Updated: May 11, 2025

Join our team at Parts Order, a seed-stage startup operating at the intersection of AI and audio engineering, with a focus on spatial and 3D audio, including Dolby Atmos. We deliver best-in-class spatial audio mastering while significantly lowering the barrier to entry for artists, audio engineers, and labels alike.

We are looking for a Machine Learning Scientist/Engineer who's ready to tackle the diverse and exciting challenges that come with our rapid growth. We are open to both full-time and part-time positions 😉

Parts Order is a fully remote company with team members distributed across multiple time zones (from PST to CET).

If you’re interested in learning more, please reach out to virgile[at]partsorder.ai!

Responsibilities:

• Design, develop, deploy, and maintain innovative ML solutions for our AI-driven spatial audio platform.
• Use expertise in research and applied ML to drive the end-to-end lifecycle from data collection and preprocessing to model training, evaluation, and deployment.
• Contribute to the creation of fresh data sets and experimental designs to support model training and validation.
• Implement and optimize digital signal processing algorithms at scale.
• Collaborate closely with a multidisciplinary team to bridge the gap between software and audio engineering, creating user-centric solutions.

Requirements:

• An appreciation for music, whether as a maker, listener, or explorer! 🧡
• Extensive experience with audio-related use cases, leveraging ML frameworks such as PyTorch or TensorFlow.
• Proficiency in writing Python in production settings (3+ years of experience preferred).
• Interest in keeping up with advancements in audio research and technology (e.g MIR, source separation, representation learning).
• Knowledge of digital signal processing and proficiency with relevant Python libraries.

Nice-to-Haves:

• Experience with cloud computing platforms (e.g., GCP, AWS).
• Experience in the development and maintenance of data pipelines and familiarity with orchestration tools (e.g. Airflow, Kubeflow).