Summary
✨ AI‑Generated
Join a fast-moving technology team building production infrastructure for AI-powered creative applications. You will design and maintain backend services, APIs, workers, orchestration systems, media pipelines, and inference infrastructure, tackling complex engineering challenges from design through deployment and optimization.
Highlights
Own challenging technical problems end-to-end while shaping scalable architecture. Work across backend services, media pipelines, AI inference, infrastructure, and production systems in a fast-moving environment.
Description
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’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.
What we offer
High ownership over important technical workBe at the forefront of AI-driven music innovationOpportunity to work on infrastructure at scaleCompetitive compensation and equityFlexibility in how you work
