Position: MLOps Engineer Tech & Data Analytics · HQ (Stockholm) ·

Location: New York

Join our global force of 500+ innovators, blending the latest in tech with the greatest in sound tracking, from our Stockholm HQ to offices in London, New York, Los Angeles, Berlin, Oslo, and Seoul. We’re an industry leader with a startup mentality. We take what we do seriously, but we don’t take ourselves too seriously. Creating and collaborating to transform the sound of streaming, content, and culture.

Come join us— and let the world feel your work.

As an MLOps Engineer, you will play a crucial role designing, building, and maintaining the core infrastructure that powers our machine learning applications. You will streamline the entire ML lifecycle, helping us pioneer solutions that tackle complex challenges in music search, recommendation, and audio modification, reliably reaching users and creators worldwide.

You will join a team of MLOps engineers within a larger, cross-functional group, collaborating closely with Machine Learning Engineers, Data Engineers, and product development teams. This role offers the chance to work with a modern tech stack while actively shaping our future by implementing next-generation technologies.

Responsibilities

Design, build, and maintain scalable infrastructure for training and serving our machine learning models using Kubernetes (GKE).
Develop and optimize CI/CD pipelines to streamline the entire ML application lifecycle, from development to production.
Implement and manage robust ML monitoring and observability solutions to ensure the reliability of our production models.
Collaborate with ML and Data Engineers to build efficient data pipelines and integrate tools like Vertex AI for pipelining and feature stores.

Requirements

Proven experience in MLOps, with a deep understanding of best practices like ML monitoring and CI/CD for machine learning.
Proficiency with Kubernetes in a production environment.
Hands‑on experience with pipeline orchestration tools such as Vertex AI Pipelines, Kubeflow Pipelines, Flyte, or Metaflow.
Infrastructure as Code skills, particularly with Terraform.
Experience with cloud‑native data processing services like Dataflow or Airflow.

Nice to have

Experience with Google Cloud Platform services like Big Query and Google Cloud Storage.
Knowledge of advanced data engineering practices.
Familiarity with observability tools for production infrastructure (e.g., Grafana, Prometheus, Open Telemetry).
Experience with serverless inference frameworks such as Seldon Core.
Familiarity with Music Information Retrieval.

Equal opportunity employer

We believe that bringing people together from different backgrounds, experiences and perspectives makes for a healthy workplace, a more successful business and a better world. We value diversity and encourage everyone to come and soundtrack the world with us.

Application

Ready to make the world feel your work? Please apply, in English, by clicking the link below.

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MLOps Engineer Tech & Data Analytics · HQ; Stockholm

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