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Mon May 06 2024
Epidemic SoundLogo

Senior Machine Learning Engineer

Epidemic Sound

Sweden (Stockholm) Remote, Hybrid


Employment Type: Full-time
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Job Description

At Epidemic Sound, we make soundtracking content simple and inspiring with unlimited access to music and sound effects. We are a force of over 500 people on a mission to soundtrack the world. Our innovative licensing model paves the way for creators - from YouTubers to the world’s most recognizable brands such as Netflix, Redbull and BBC. 

At Epidemic, we are expanding our central Machine Learning Engineering team. We have a hybrid organization where Machine Learning Engineers (MLEs) work in either a central team, or in product teams. Over the past five years, we have built a number of ML-powered applications that are highly appreciated by users. From intelligent search features, such as semantic search and audio-based similarity search, and recommender systems to metadata tagging and music preview systems.

We are now welcoming a skilled Senior Machine Learning Engineer to join our central MLE team. You will report to the Head of Machine Learning and work together with colleagues on a broad range of problems going across the company. This involves the full range, from learning what problems are suitable to solve with ML, getting a deep understanding of those problems, as well as solving the problems and delivering high-quality models and code. We also work with the MLOps team, on evolving the company’s ML platform and we frequently collaborate with MLEs in product teams.

In this role, you will design, develop and deploy machine learning applications that will have a significant impact across our organization.

How you will make an impact:

  • Collaborate with cross-functional teams to understand their problems and translate them into technical solutions
  • Build prototypes, conduct experiments, and operationalize solutions at scale
  • Build, deploy and maintain pipelines and backend services together with your team and across functions
  • Continue to foster a curious and explorative culture in a collaborative environment: e.g. pre-studies, study groups and theory Tuesdays
  • Initiate, drive and own projects together with your team
  • Improve technical processes and tooling
  • Be part of building capabilities for downstream use
  • Coach and mentor team members and staff from other teams

We believe that to succeed in this role, you have experience in some/all:

  • Computer Science and Machine Learning with results in academia or industry
  • Working as a Machine Learning Engineer
  • Architecting, developing, deploying and monitoring software systems, preferably in Python
  • GCP or similar cloud solutions
  • MLOps best practices and relevant tools

Furthermore, it would be music to our ears if you have real world experience in:

  • Adapting/Fine-tuning foundation models to downstream applications
  • Applying methods from Music Information Retrieval (MIR), especially using modern architectures

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.

Do you want to be a part of our fantastic team? Please apply, in English, by clicking the link below.

Apply for this job
🍀 Please mention you found the job on EU Data Jobs. It helps us reach out to more companies. We wish you lots of success! 🍀

About the Company

Epidemic Sound has transformed the soundtracking experience for global brands and professional creators, with an expansive catalog of world-class music and sound effects that's seen and heard over 2.5 billion times a day around the globe.

Providing a direct license model that comes with all rights included and next-generation soundtracking tools, Epidemic Sound empowers creators to unlock more feeling in everything they create and share their stories with the world. Epidemic Sound continuously enriches its world-class catalog of music by teaming up with artists, composers, and producers to create tracks spanning all genres, while supporting them financially and creatively.


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