Who we are

AIMS is a music-tech scaleup providing leading AI-powered music search and discovery solutions. We work with the world's biggest music and media companies — including Warner Chappell PM, Universal PM, Hipgnosis and 60+ others. We're entirely self-funded and profitable, operating as a remote-first team of music and tech lovers based all over the world.

Our story has been featured in Forbes and CzechCrunch.

Why we need you

As we push the boundaries of AI in music tech, the journey from a successful research experiment to a reliable production service needs to become faster and more seamless. We're looking for an experienced MLOps engineer to bridge this critical gap and build the infrastructure that powers our next generation of AI features.

You will drive the ML lifecycle, designing and implementing efficient model serving solutions, and composition systems for complex inference graphs. You'll also architect and build our internal research platform and enhance Python tooling to accelerate experimentation and improve the researcher experience (DX). By establishing MLOps best practices and optimizing workloads (including GPU utilization), you'll directly impact our innovation speed, reliability, and performance.

What we use

Core ML: Python, PyTorch, CUDA, Hugging Face libraries, NumPy, scikit-learn, Jupyter
MLOps & Workflow: KServe, GitLab CI/CD, Dagster, MLflow, vLLM, MongoDB, Elasticsearch
Infrastructure & Orchestration: Kubernetes, Docker, Ceph, Ansible, Linux, NVIDIA GPUs, Prometheus, Grafana

You'll fit right in if you've mastered:

Python development with strong software engineering practices
Machine learning and deep learning at scale
Containerization and orchestration (Docker, Kubernetes) for ML workloads
Model serving architectures and frameworks (including KServe)
ML model deployment pipelines and continuous delivery for ML systems
Building and maintaining ML platforms and tooling with a focus on researcher experience (DX)
Experiment tracking and model versioning principles
CI/CD automation for ML systems (GitLab CI/CD or similar)
GPU optimization for ML workloads
Establishing and promoting MLOps best practices
Distributed training management and optimization
English good enough to communicate effectively with the team
Ability to work effectively within European timezones and travel occasionally for in-person meetings

What you don't need to know, but we appreciate if you do or if you're willing to learn

Experience with workflow orchestration tools (e.g., Dagster, Kubeflow)
Knowledge of large-scale data processing principles and common frameworks (like Ray, Spark, etc.)
Experience with vector databases and search systems
Experience with a compiled language (C, C++, Rust, Go)
Familiarity with autoscaling ML inference services
Understanding of ML system testing and quality assurance
Experience with ML performance monitoring and observability
Familiarity with modern AI paradigms (RAG, Agents)
Understanding of feature store design and implementation
Background in music technology, audio processing, or digital signal processing
You like music :)

What we offer

Competitive salary based on skills and experience
Friendly teammates (we won't even judge you for using light mode in your IDE)
Opportunity to build cutting-edge MLOps systems and see the direct impact of your work
Chance to be at the forefront of the AI revolution in the music business
Fully remote work possible (we have offices in Prague and Copenhagen)

Ready to build the future of AI in music search and discovery? Meet our Head of Research Viktor and ask anything you'd like to know.

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