Why This Job is Featured on The SaaS Jobs
This Staff Research Engineer role stands out in the SaaS ecosystem because it sits at the intersection of product-scale delivery and frontier applied research. The remit is not limited to model prototyping; it includes making generative music systems reproducible, efficient, and deployable within a widely used subscription platform. That combination reflects a mature, platform-oriented SaaS environment where research outcomes are expected to translate into reliable user-facing capabilities.
For a long-term SaaS career, the work builds durable leverage in areas that repeatedly matter across modern software businesses: production ML, performance engineering, and operationalizing experimentation. Improving distributed training, profiling inference, and building internal tooling are experiences that transfer directly to other SaaS domains adopting AI features, especially where latency, cost, and iteration speed become product constraints rather than research footnotes.
This position is best suited to engineers who prefer deep technical ownership across the stack and who enjoy partnering closely with researchers and platform teams. It fits professionals who want to be evaluated on engineering rigor, scalability, and deployment discipline, while still staying close to new model ideas and the practical realities of shipping them.
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 Staff 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 $215,136 - $307,337 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.
