We are looking for:

As a Machine Learning Engineer, your main tasks will be to develop and deploy machine learning models within a given domain, whilst working, in close collaboration with our diverse group of machine learning experts, on creating a good infrastructure for machine learning pipelines in production. We are a diverse group of ML engineers, scientists, and architects who all strive to build the best possible streaming service.

Responsibilities:

• Build, evolve, and scale state-of-the-art machine learning models and systems

• Deploy and maintain ML models in production

• Work with other machine learning / deep learning researchers and backend engineers to implement scalable solutions for solving complex problems

We expect that you:

• Are fluent written/oral English 

• 3+ years of work experience as a Machine Learning Engineer or from a similar role

• Experience in making machine learning pipelines and systems that handle large scale data

• Knowledge of cloud computing for machine learning purposes

• DevOps mindset and a flair for automation

We imagine you have some of the following qualifications

• Good understanding of Python (Pandas, Numpy, Scipy, Sklearn, Pyspark, SQL, Hive)

• Experience with Machine Learning technologies: Scikit, Torch, TensorFlow, Spark MLlib etc.

• Experience with the Amazon AWS platform and experience with integrating applications and platforms with AWS (e.g. EC2, EMR, S3, Lambda, EKS, ECS, ECR, Neptune)

• Experience with training online/incremental models on data streams

• Docker - experience in both reusing existing Docker images and dockerizing services from scratch e.g. Dockerizing a Flask API, training an ML model using Keras/Tensorflow, serving an ML model using TensorFlow Serving, ONNX, etc

• Experience with containerized ML pipelines: e.g. Kubeflow, Amazon Sagemaker, MLFlow

• Experience with the development of large scale Recommendation Systems

• Experience with running/deploying data processing flows in a distributed manner using Spark

• Knowledge about data about music