ML Engineer

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, deliver inspiring experiences, continuously innovate and empower our communities, while also serving our planet.

Help us reach our goal in making the future of music diverse, inclusive and exciting!

About The Team

You will join the Research team, a cross‑functional group of machine‑learning and DSP specialists that fuels innovation across the group of Native Instruments brands. Our researchers invent novel audio‑ML technology; your role bridges the gap between those prototypes and shipping products. Based in our brand‑new Madrid hub, the team collaborates daily with colleagues in Boston, Berlin, and London, enabling AI‑powered features in flagship instruments, synths, mixing, mastering, and other creative audio production and music creation tools.

Your Contribution

We are seeking a seasoned ML Engineer with a strong background in bringing machine learning models from research to robust, production-ready applications. You will be instrumental in the following areas:

• Drive the "last-mile" of ML development: Optimize, quantize, and compress models for efficient real-time inference using ONNX Runtime and C++ architectures, ensuring performance in a production environment.
• Drive the successful integration of ML features: Partner with product-engineering teams by delivering high-quality example code and providing hands-on mentorship through pair programming and code review. Your goal is to upskill our teams and ensure ML features are deployed seamlessly and efficiently.
• Contribute to performance tuning and profiling: Rigorously ensure ML-enabled features meet strict CPU/latency budgets for real-time audio applications.
• Serve as a floating enabler and collaborator: Engage early in the research lifecycle to provide critical feedback on production feasibility, facilitate technology transfer between teams, and resolve impediments to delivery timelines for production features.
• Champion best-practice ML workflows: Guide the adoption of robust MLOps practices, including data versioning, CI/CD pipelines, and performance profiling, across multiple product teams.
• Mentor researchers and junior engineers: Provide expert guidance on practical deployment topics, significantly raising the organization’s overall ML engineering maturity.

Our Ideal Candidate

We are looking for a highly skilled ML Engineer with a proven track record of deploying machine learning models in a production setting.

• Solid software-engineering foundation with significant experience delivering ML-powered features for desktop applications (audio domain highly desirable). This includes a deep understanding of software development best practices for production systems.
• Comfortable working within a cross-functional, multicultural environment with excellent English communication skills, essential for collaborating with the entire product team (including product manager, product designers, engineers, and research, among other functions).
• Proficiency in Python for prototype collaboration and C++ for robust production integration, with solid knowledge of ONNX Runtime or equivalent industry-standard inference frameworks.
• Hands-on expertise in model-size reduction techniques (quantization, pruning, knowledge distillation) and runtime performance optimization for deployed models.
• Familiarity with audio DSP concepts and real-time constraints, or a demonstrable eagerness to acquire these skills for production audio applications.
• Experience setting up or improving ML workflows: version-controlled datasets, automated testing, CI pipelines, and performance monitoring, to ensure model reliability and maintainability.

Our Benefits

• Trust-based working hours
• Holidays: 25 days paid holiday per year
• Global Travel Insurance coverage
• Public Health, Pension & Disability Insurance coverage
• Free software downloads and reduced prices on hardware