GRAI is reimagining how music is discovered and shared, created around the social layer of it. We're pre-launch, moving fast, and building the infrastructure for how the next generation of artists connects with their audience.
The roleYou’ll build the systems that make GRAI’s AI music products work. From backend services and media pipelines to model inference and infrastructure, you’ll own hard technical problems end-to-end and help shape the architecture as we scale.
What you’ll ownDesign, build, test, and maintain production software used in GRAI’s AI music products.
Build backend services, APIs, workers, internal tools, and orchestration systems for media and AI pipelines.
Work with audio/video processing systems, including formats, codecs, transcoding, metadata, storage, streaming, and delivery.
Build and optimize systems around model inference, job queues, scheduling, batching, caching, retries, and observability.
Strong software engineering experience building production systems.
Strong Python experience.
Solid understanding of data structures, algorithms, complexity, and practical performance tradeoffs.
Experience with distributed systems, backend services, APIs, queues, databases, object storage, and cloud infrastructure.
Ability to debug complex issues across code, infrastructure, network, storage, and service boundaries.
Experience writing clean, tested, maintainable code.
Ability to work independently, make good technical decisions, and own projects end-to-end.
Experience with one or more additional languages: Go, Rust, C++, Java, TypeScript, or similar.
Experience with audio, video, media processing, codecs, containers, streaming, transcoding, FFmpeg, or similar systems.
Experience with ML infrastructure, model serving, GPU workloads, inference optimization, batching, or distributed training/inference systems.
Experience with cloud infrastructure, Kubernetes, Docker, Terraform, AWS/GCP/Azure/Nebius, or similar.
Experience with data pipelines, object storage, large-scale file processing, WebDataset, S3-compatible storage, Spark/Ray/Polars/DuckDB, or similar.
High ownership over important technical work
Be at the forefront of AI-driven music innovation
Opportunity to work on infrastructure at scale
Competitive compensation and equity
Flexibility in how you work
Skills Required
- Strong software engineering experience building production systems
- Strong Python experience
- Understanding of data structures, algorithms, complexity, and practical performance tradeoffs
- Experience with distributed systems, backend services, APIs, queues, databases, object storage, and cloud infrastructure
- Ability to debug complex issues across code, infrastructure, network, storage, and service boundaries
- Experience writing clean, tested, maintainable code
- Ability to work independently, make good technical decisions, and own projects end-to-end
- Experience with an additional language such as Go, Rust, C++, Java, or TypeScript
- Experience with audio, video, media processing, codecs, containers, streaming, transcoding, or FFmpeg
- Experience with ML infrastructure, model serving, GPU workloads, inference optimization, batching, or distributed training and inference systems
- Experience with cloud infrastructure, Kubernetes, Docker, Terraform, AWS, GCP, Azure, or Nebius
- Experience with data pipelines, object storage, large-scale file processing, WebDataset, S3-compatible storage, Spark, Ray, Polars, or DuckDB
What We Do
GRAI is an AI music research lab based in Warsaw, Poland, dedicated to building foundation models and interaction primitives for the future of music. The company develops AI-powered tools that enable users to interactively remix, transform, and share songs, aiming to make music more social and interactive while ensuring artists maintain control and potentially benefit from new royalty streams.
