Job description

Join a new, high-velocity "startup-style" Applied Research team within TechEng dedicated to transformational audio bets for the next decade of Pixel and Buds. You will lead exploratory, non-timeline-based research into Open Ear Inteligibility and Superhuman Hearing for humans and machines. We operate under a "Shielded but Connected" model—protected from the gravitational pull of daily product cycles to focus on pure feasibility and first-principles innovation. In this role, you will adopt an impact-first philosophy, ruthlessly prioritizing massive user differentiation to prove the "impossible" and define the foundational elements of agentic audio experiences. If you are an audio architect who grows in lean environments and wants to build the future of sound, this is your team. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . • 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. • 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.). 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.

Requirements

Must-haves

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
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.
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.).
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.).

Nice-to-haves

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningpythonml

Platform: google
Effort: medium