ML Research Scientist, Audio Algorithms

• Develop new and advanced algorithms for "moonshot" audio initiatives specifically real time perception for humans and AI operating outside the gravitational pull of immediate product timelines.
• Architect first-principles algorithms that bridge the gap between theoretical research and validated prototypes, proving feasibility before scaling.
• Adopt an impact-first philosophy, prioritizing massive user differentiation and maintaining the agility to pivot when technical paths do not yield high-order breakthroughs.
• Foster a "Shielded but Connected" environment, protecting the team's research velocity while providing technical guidance to core execution teams on future-decade issues.
• Collaborate alongside a small group of researchers (including Principal-level leadership) to define the foundational elements of next-generation agentic audio.

Minimum qualifications:

• PhD in Computer Science, a related field, or equivalent practical experience.
• 2 years of experience leading a research agenda.
  
• Experience developing and training machine learning algorithms specifically for audio applications (e.g., neural noise suppression, acoustic modeling, or speech enhancement).
• Experience with deep learning frameworks such as JAX or TensorFlow applied to signal processing tests.
• One or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Preferred qualifications:

• Experience in algorithm implementation and prototyping using C++ and Python.
• Experience taking theoretical ML-audio research ideas and implementing functional product proofs-of-concept.
• Experience navigating high-ambiguity research environments where the priority is delivering massive user differentiation over a specific method.
• Experience in developing foundational elements for ML based voice/audio algorithms.

• PhD in Computer Science, a related field, or equivalent practical experience.
• 2 years of experience leading a research agenda.
  
• Experience developing and training machine learning algorithms specifically for audio applications (e.g., neural noise suppression, acoustic modeling, or speech enhancement).
• Experience with deep learning frameworks such as JAX or TensorFlow applied to signal processing tests.
• One or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).