{"id":1237665,"url":"https://alion.io/job/google-ml-research-scientist-audio-algorithms","title":"ML Research Scientist, Audio Algorithms","company":{"id":82,"name":"Google","domain":"google.com","url":"https://alion.io/company/google","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":75,"open_postings":110,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":30,"computed_at":"2026-10-07T05:47:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Irvine, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":104000,"max_usd":236000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":290},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"JAX","optional":false},{"name":"Machine Learning","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"C++","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-09-25T12:41:03Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-07T23:32:44Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"About the job\nJoin 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.\nWe 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.\nIndividual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits\nLearn more about benefits at Google.\nResponsibilities\nDevelop 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.\nArchitect first-principles algorithms that bridge the gap between theoretical research and validated prototypes, proving feasibility before scaling.\nAdopt an impact-first philosophy, prioritizing massive user differentiation and maintaining the agility to pivot when technical paths do not yield high-order breakthroughs.\nFoster a \"Shielded but Connected\" environment, protecting the team's research velocity while providing technical guidance to core execution teams on future-decade issues.\nCollaborate alongside a small group of researchers (including Principal-level leadership) to define the foundational elements of next-generation agentic audio.\nQualifications\nMinimum qualifications:\nPhD in Computer Science, a related field, or equivalent practical experience.\n2 years of experience leading a research agenda.\nExperience developing and training machine learning algorithms specifically for audio applications (e.g., neural noise suppression, acoustic modeling, or speech enhancement).\nExperience with deep learning frameworks such as JAX or TensorFlow applied to signal processing tests.\nOne or more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).\nPreferred qualifications:\nExperience in algorithm implementation and prototyping using C++ and Python.\nExperience taking theoretical ML-audio research ideas and implementing 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