About GRAI
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 role
You’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 own
- Design, 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.
What we offer
- 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
What we’re looking for
- 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.
Especially relevant experience
- 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.
