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The Role
Develop novel machine learning methods and architectures for generative conversational speech-to-speech models. Research speech synthesis and recognition, improve model quality and realism, collaborate on data and infrastructure, and help scale proven research into production pipelines and Spotify products.
Summary Generated by Built In
he Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we're behind some of Spotify's most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you'll keep millions of users listening by making great recommendations to each and every one of them.
Within Personalization, the Speak Team owns the development of Spotify's state-of-the-art speech models, contributing to speech recognition, speech synthesis, and speech-to-speech models. We craft voice models that match human-level emotional expressiveness, so we can deeply engage our listeners and support creators at scale. Our groundbreaking work on speech synthesis relies on state-of-the-art deep learning methods and evaluation techniques, highly efficient data processing and model serving, and capturing audio of outstanding quality from our voice talent pool.
We're looking for a senior applied research scientist with experience in developing novel ML techniques and architectures and with a strong interest in working across a full production pipeline to produce state-of-the-art generative conversational speech-to-speech models. You'll collaborate with our engineering teams to help develop our production pipelines, explore new ideas and methods to improve quality, understanding and realism, as well as push the frontiers of what is possible with our speech technology.
What You'll Do
- Develop and experiment with new methods for speech synthesis and speech recognition, along with end-to-end approaches, building on the latest research and ideas.
- Work towards the expansion of our speech use-cases targeting different markets and products.
- Be part of a highly motivated research team dedicated to building and creating models at scale to power the Spotify platform.
- Champion best practices for research and development, sharing your knowledge and experience with other researchers within Speak.
- Collaborate with our engineering and data teams on ideas requiring new infrastructure or new high-quality data, as well as to help improve our speech recognition and speech synthesis pipelines, and help turn proven ideas into scalable products.
Who You Are
- You have a strong background in ML (PhD degree on top of professional experience), and
- experience in working with any of the following: transformers, GANs, diffusion models, flow matching, VAEs, audio codecs.
- You have experience in developing generative models for speech synthesis, speech recognition, audio/music, natural language processing, or computer vision.
- You have strong experience with Python, particularly PyTorch.
- You have strong communication skills and the ability to explain technical ideas with clarity to technical and non-technical people alike.
- You have experience in an academic or professional setting conducting high-quality research.
Where You'll Be
- This role is based in New York City.
- We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home
The United States base range for this position is $169,157 - $241,653 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, a monthly meal allowance, 23 paid days off, 13 paid flexible holidays. These ranges may be modified in the future.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Skills Required
- PhD degree with professional experience in machine learning
- Experience with transformers, GANs, diffusion models, flow matching, VAEs, or audio codecs
- Experience developing generative models for speech synthesis, speech recognition, audio/music, natural language processing, or computer vision
- Strong Python experience, particularly with PyTorch
- Strong communication skills and ability to explain technical ideas to technical and non-technical audiences
- Experience conducting high-quality research in an academic or professional setting
Spotify Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Spotify and has not been reviewed or approved by Spotify.
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Flexible Benefits — Employees consistently praise the total compensation package beyond base salary, highlighting a mix of RSUs, cash incentives, and stipends alongside core pay. The package is described as flexible and customizable through equity choices (e.g., RSUs, options, cash) that can be tailored for long-term wealth building.
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Leave & Time Off Breadth — Time-off offerings are repeatedly highlighted as substantial, including generous vacation, paid sick days, volunteer time, and flexible holidays. These policies are framed as a meaningful part of the overall rewards experience beyond salary.
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Healthcare Strength — Health coverage is portrayed as comprehensive, spanning medical, dental, vision, life insurance, disability coverage, and mental health support. Additional employer contributions to HSAs are cited as strengthening the overall health and wellness value proposition.
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The Company
What We Do
Spotify transformed music listening forever when it launched in Sweden in 2008. Discover, manage and share over 50m tracks for free, or upgrade to Spotify Premium to access exclusive features including offline mode, improved sound quality, and an ad-free music listening experience.
