Machine Learning Performance Engineer
New Yesterday
If you're excited by adversarial modelling, anomaly detection, and building systems that defend one of the world's leading streaming platforms, we'd love to hear from you.
Contribute to designing, building, evaluating, shipping, and refining Spotifys anti-fraud product by hands-on ML development
Collaborate with a multi-functional team spanning data science, product management, and engineering to combat fraud
Help drive optimisation, testing, and tooling to improve quality
Be part of an active group of machine learning practitioners in your mission and across Spotify
Conduct analyses to gain insights on fraudulent behaviours and trends
Be responsible for monitoring the quality and performance of the squad's ML models
You have a strong background in machine learning, theory, and practice
You are comfortable explaining the intuition and assumptions behind ML concepts
You have hands-on experience implementing and maintaining production ML systems in Python, Scala, or similar languagesExperience with TensorFlow or Pytorch
You are experienced with building data pipelines, and you are self-sufficient in getting the data you need to build and evaluate your modelsYou preferably have experience with cloud platforms like GCP or AWS
You care about agile software processes, data development, reliability, and focused experimentation
There will be some in person meetings, but still allows for flexibility to work from home.
We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - were here to support you in any way we can.
Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the worlds most popular audio streaming subscription service.
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- Location:
- London
- Job Type:
- FullTime
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