Data Engineer for Audio ML Research

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For over 25 years, Native Instruments has been at the forefront of sonic innovation. Guided by our mission to inspire and enable creators to express themselves, we develop integrated audio hardware and software solutions for musicians, producers, engineers, and DJs of all genres and levels of experience.

Native Instruments embraces diversity and a respect for all people. We are proud to be an equal opportunity employer and we believe the foundation of our dynamic and pioneering spirit starts with a fair and inclusive culture. At Native Instruments we value teamwork and passion, delivering inspiring experiences, continuously innovating and empowering our communities, while also serving our planet.

All applicants will receive equal consideration for employment at Native Instruments and we encourage everyone to apply – regardless of gender identity, race, color, religion, sex, sexual orientation, national origin, genetics, disability, age, or any other characteristic protected by law.

Help us reach our goal in making the future of music diverse, inclusive and exciting! We encourage you to submit your application without the requirement for a photograph, identifying factors or personal status information.

You will join the Research team, partnering closely with Product, Design, and Engineering teams across Native Instruments brands. As our Data Engineer, you will build and operate the pipelines and datasets that make high-quality audio and symbolic audio available for experimentation and shipped products, while keeping data secure and compliant. The role is based in our Madrid hub, collaborating daily with colleagues in Boston, Berlin, and London.

As the engineering owner of our research datasets, you will transform raw data into the foundation for our most innovative AI-powered features, directly increasing the creative capacity of our Research Engineers.

• Own Data Governance: Design and implement clear schemas, access controls, and governance to keep our audio data secure, compliant, and discoverable.
• Build the Foundation: Design and run scalable data pipelines on AWS to ingest vast internal audio libraries and generate novel training data by programmatically controlling our virtual instruments and effects.
• Shape the Sound: Actively curate and shape the sonic character of our datasets through expert processing, augmentation, and quality validation, directly influencing the output of our ML models.
• Enable Reproducibility: Publish versioned, documented, and traceable datasets to empower reproducible research and improve team efficiency with self-serve tools and robust monitoring.
• Connect to Customers: Develop pipelines that translate anonymized product telemetry into actionable insights on how our models perform in the wild.

• Significant years of data engineering experience with expertise in data governance, including schema design, access controls, and compliance management.
• Strong proficiency in Python and SQL.
• Hands-on expertise building and maintaining scalable data pipelines on AWS, particularly using S3 and running Python jobs in containers or on Linux nodes.
• A good working knowledge of audio datasets, including concepts like sampling rates, formats, and quality measures (e.g., S/N ratio, THD).
• Hands-on experience with music production tools and comfort in automating VSTs, instruments, or synthesizers to generate audio.
• Experience with data-centric MLOps practices like dataset versioning, experiment tracking, and data validation for ML reproducibility.
• A background in data processing and augmentation for machine learning; experience with MIDI or audio feature extraction is a significant plus.
• A clear and collaborative communicator, capable of partnering effectively with cross-functional teams.
• A genuine love of music and audio production and enthusiasm for building the future of creative tools.

• Remote First: We offer a range of options that allow you to work in a way that suits your lifestyle, either at one of our workspaces, a hybrid arrangement, or fully remote.
• Shared workspace in London: Landmark Spaces
• Workation: Work remotely anywhere in the world for up to 4 weeks per year
• Flexible work model from one of our entity locations
• Trust-based working hours
• Holidays: 25 days paid holiday per year which increases with tenure
• Healthcare: Public health care with NHS supplemented with Simply Health. This involves contributions for dental and optical healthcare, for example
• Pension: Employees can choose an auto enrolment scheme or can bring their own SIPP with them
• Free software downloads and reduced prices on hardware
• Employee Assistance Program for your well-being

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