National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Sensing Research Scientist

Sonos, Inc.
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Research Scientist: Data Science and Machine Learning AIP

RF Data Scientist Research Engineer

Senior Scientist – Functional materials, thin films and coatings

Senior Scientist – Functional materials science and particle chemistry

12 Month Internship - Data Scientist

Laboratory Technician (Sensor testing laboratory)

At Sonos we want to create the ultimate listening experience for our customers and know that it starts by listening to each other. As part of the Sonos team, you’ll collaborate with people of all styles, skill sets, and backgrounds to realize our vision while fostering a community where everyone feels included and empowered to do the best work of their lives.

Audio technology research at Sonos sits at the intersection of audio signal processing, machine learning, acoustics, and audio perception. With the launch of the Sonos Ace, the company has taken a big first leap into the headphones space.

The Hearables Innovation Team at Sonos are looking for a Research Scientist in Sensing to join us in inventing the personal listening experiences of the future.

What you’ll do

Advanced technologies play a central role in the future of the Sonos hearables experience, covering a range of research areas including spatial audio, adaptive noise cancellation, telephony, wearable computing, and sensing.

As a Research Scientist in Sensing for Hearables, you will be responsible for researching, developing, and evaluating sensor technologies, signal processing and machine learning methods, and interaction concepts. This will include identifying the emerging opportunities presented by new methods and developments in the field and taking ideas from early concepts to scalable, productisable solutions.

Skills You'll Need

Basic Qualifications

  • Ability to formalize, analyze and solve complex problems.

  • Demonstrable expertise and research experience in one or more of the following areas:

    • Sensor processing, sensor fusion

    • Brain-computer interaction

    • Mobile and wearable computing

    • Machine learning

    • Spatial computing

    • Augmented hearing and assistive technologies

  • Ph.D. in audio, covering one of the above areas or a related topic.

  • Knowledge of fundamental DSP and ML techniques for sensing and sensor fusion (e.g. state estimation, data pre-processing and modeling).

  • Comfortable working with and/or designing prototype hardware systems

  • Comfortable using Python or Matlab, and version control tools.

  • Ability to clearly communicate, present, and disseminate research findings to both specialist and general audiences.

Preferred Qualifications

  • Musical and/or critical listening background (playing, producing, engineering).

  • Track record of published papers in relevant peer-reviewed journals or conferences (e.g. ICML, SENSORS, UIST, CHI, CVPR, Ubicomp, MobiCom, ICASSP, Interspeech).

Your profile will be reviewed and you'll hear from us once we have an update. At Sonos we take the time to hire right and appreciate your patience.

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How to Get a Better Machine Learning Job After a Lay-Off or Redundancy

Redundancy in machine learning can feel especially frustrating when your role was technically advanced, strategically important, or AI-facing. But the UK still has strong demand for machine learning professionals across fintech, healthtech, retail, cybersecurity, autonomous systems, and generative AI. Whether you're a research-oriented ML engineer, production-focused MLOps developer, or applied scientist, this guide is designed to help you bounce back from redundancy and find a better opportunity that suits your goals.

Machine Learning Jobs Salary Calculator 2025: Figure Out Your True Worth in Seconds

Why last year’s pay survey is useless for UK ML professionals today Ask a Machine Learning Engineer wrangling transformer checkpoints, an MLOps Lead firefighting drift alarms, or a Research Scientist training diffusion models at 3 a.m.: “Am I earning what I deserve?” The honest answer changes monthly. A single OpenAI model drop doubles GPU demand, healthcare regulators release fresh explainability guidance, & a fintech unicorn pays six figures for vector‑search expertise. Each shock nudges salary bands. Any PDF salary guide printed in 2024 now looks like an outdated Jupyter notebook—missing the gen‑AI tsunami, the surge in edge inference, & the UK’s new Responsible‑AI framework. To give ML professionals an accurate benchmark, MachineLearningJobs.co.uk distilled a transparent, three‑factor formula that estimates a realistic 2025 salary in under a minute. Feed in your discipline, UK region, & seniority; you’ll receive a defensible figure—no stale averages, no guesswork. This article unpacks the formula, highlights the forces driving ML pay skyward, & offers five practical moves to boost your value inside the next ninety days.

How to Present Machine Learning Solutions to Non-Technical Audiences: A Public Speaking Guide for Job Seekers

Machine learning is driving change across nearly every industry—from retail and finance to health and logistics. But while the technology continues to evolve rapidly, the ability to communicate it clearly has become just as important as building the models themselves. Whether you're applying for a junior ML engineer role, a research position, or a client-facing AI consultant job, UK employers increasingly expect candidates to explain complex machine learning solutions to non-technical audiences. In this guide, you’ll learn how to confidently present your work, structure your message, use simple visuals, and explain the real-world value of machine learning in a way that makes sense to people without a background in data science.