Mirelo AI

Research Scientist

• Design, implement and train large-scale multimodal generative models for audio generation (diffusion and/or autoregressive models).
• Explore new modeling ideas for audio generation (music, sound, speech) while taking inspiration from the language and image domains.
• Develop and experiment with post-training for new capabilities (fine-grained control, in/out-painting, editing, …)
• Conduct rigorous ablation studies, get actionable insights and communicate results to the team to discuss new research directions.
• Contribute hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment.

Research Scientist – Model Team

Infrastructure Engineer

• Focus on the full training stack - profiling GPU behavior, debugging training pipelines
• Improve throughput, choosing the right parallelism strategies
• Design the infrastructure for efficient model training at scale
• Work across cluster management, model training, efficient data pipelines, inference and optimizing PyTorch code

Training Infrastructure Engineer

Full-stack Engineer

• Building and maintaining products on top of cutting-edge generative AI models for music and sound.
• High degrees of ownership: responsible for shipping end-to-end features across the front and back ends of the stack.
• Collaborate closely with research and growth/sales teams to identify practical opportunities and turn them into actionable engineering tasks.
• Work comfortably with tech stack: TypeScript, React, Tailwind, shadcn, and Convex as backend.
• Use AWS Lambda and S3 for integrations, automation, and deployment workflows.
• Use modern AI-assisted development tools (Cursor, Claude Code) to maintain high craftsmanship while moving swiftly.
• Communicate clearly with teammates to drive the technical direction jointly.

Full-Stack Software Engineer

• Develop and implement new neural audio codecs for sound, music and speech that push the state-of-the art in sound quality and are optimized for the the use case of generative models.
• Think about the specific challenges that arise when the codec is primarily used as a latent representation in the context of generative audio models (in the end, the ultimate goal is to build the best audio generative models)
• Explore the trade-offs of continuous (as typically used for diffusion models) vs. discrete audio representations (as typically used for autoregressive models).
• Develop benchmarking pipelines for codec evaluation
• Conduct initial experiments with generative models to verify that a new candidate codec is actually useful for our downstream tasks.

Research Scientist – Model Team

Training Infrastructure Engineer

Full-Stack Software Engineer

Research Scientist – Audio Codec