Why This Job is Featured on The SaaS Jobs
This Senior Research Engineer role sits at an intersection that is increasingly central in SaaS: applied AI work that must ship reliably inside a large-scale subscription product. The remit spans research enablement, production integration, and performance work, which reflects how mature SaaS platforms operationalize machine learning beyond prototypes. The “artist-first” framing also signals a domain where product constraints, rights considerations, and user trust shape technical choices, not just model quality.
For a SaaS career, the lasting value is the exposure to end-to-end ML systems: building reproducible experimentation pipelines, improving distributed training efficiency, and partnering with platform and product teams to deploy models at real usage volume. That combination develops judgment around observability, reliability, and iteration cadence, skills that transfer across SaaS companies running recommendation, personalization, or generative features. It also creates a strong foundation in internal tooling and developer experience, a common leverage point in scaling ML organizations.
The role is best suited to engineers who enjoy working close to research while staying accountable to production outcomes. It favors a working style that balances deep debugging and performance profiling with cross-team collaboration and asynchronous communication across time zones. It will resonate with candidates who want their ML engineering work to influence a consumer SaaS surface area rather than remain in a standalone research environment.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
We are seeking a Senior Research Engineer to join our Artist-First AI Music lab. Our team pioneers and advances state-of-the-art generative technologies for music that create breakthrough experiences for fans and artists. We invent entirely new listening experiences that center and celebrate artists and creatives. All of our products will put artists and songwriters first, through these four principles:
Partnerships with record labels, distributors, and music publishers: We’ll develop new products for artists and fans through upfront agreements, not by asking for forgiveness later.
Choice in participation: We recognize there’s a wide range of views on use of generative music tools within the artistic community. Therefore, artists and rightsholders will choose if and how to participate to ensure the use of AI tools aligns with the values of the people behind the music.
Fair compensation and new revenue: We will build products that create wholly new revenue streams for rightsholders, artists, and songwriters, ensuring they are properly compensated for uses of their work and transparently credited for their contributions.
Artist-fan connection: AI tools we develop will not replace human artistry. They will give artists new ways to be creative and connect with fans. We will leverage our role as the place where more than 700 million people already come to listen to music every month to ensure that generative AI deepens artist-fan connections.
For more information, see this press release! https://newsroom.spotify.com/2025-10-16/artist-first-ai-music-spotify-collaboration/
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What You'll Do- Closely collaborate with research scientists. Work side-by-side to turn new research ideas into well-engineered experiments, ensuring efficiency, clarity, and reproducibility in every implementation.
- Improve model training pipelines. You’ll debug distributed training, optimize data loading at massive scale, and ensure smooth scaling across compute environments.
- Optimize performance. You’ll profile and accelerate existing training and inference code to make experiments faster and production systems more responsive.
- Integrate models into production environments. You’ll work directly with platform and product teams to deploy models into the hands of hundreds of millions of Spotify’s users.
- Incorporate state-of-the-art research. You'll translate models and techniques described in the literature into robust, well-engineered prototypes.
- Maintain a high-quality codebase. You’ll enforce clear structure, consistency, and testing practices to support long-term maintainability on a codebase shared between members of a fast-paced globally distributed team.
- Enhance researcher experience. You’ll build internal tooling, libraries, and workflows to make experimentation, debugging, and deployment more efficient for the whole team.
Who You Are- You have experience training or fine-tuning large machine learning models on GPUs using PyTorch or similar frameworks.
- You have experience working with cloud platforms like Google Cloud Platform, AWS, or Microsoft Azure.
- You understand how to debug problems in machine learning training code.
- You communicate effectively with global teams and are ready to work both face-to-face and asynchronously with collaborators on multiple continents.
- You have experience optimizing code for performance and can make GPUs “go brrr” (train at maximum efficiency).
- You learn new concepts and technologies quickly and keep up to date with the rapid pace of development in machine learning and AI.
- You are resourceful and proactive; when faced with blockers, you seek out solutions through research, experimentation, and collaboration.
- You’re not afraid to dig deep into the stack: working with lldb, NVIDIA Nsight, or other low-level debugging tools is a plus.
- You have a solid grasp of computer science concepts like type systems, compilers, parallelism, thread safety, encapsulation, and the like.
- You have an interest in learning more about audio processing and music information retrieval and you're excited about building amazing products that use these technologies.
Where You'll Be- We offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location.
- This team operates within the Eastern Standard time zone for collaboration.
- Core working hours are CET 3pm-6pm / EST 9am-12pm.
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The United States base range for this position is $176,166 - $251,666 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.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
