Machine Learning Engineer
PressReader is the “Netflix” of newspapers and magazines. We partner with thousands of publishers in over a 100 countries to make almost 10000 publications into the platform and make them available for users at PressReader.com and within native apps.
PressReader R&D team builds state-of-the-art natural language processing and personalisation models to drive content personalization and event analytics across several products for consumers and businesses, including the award-winning News360 mobile app, and the NativeAI publisher analytics suite. We provide personalised news recommendations tracking millions of different interests, and build publisher analytics tools that drive content strategies reaching millions of readers every day.
We are looking for a highly motivated Machine Learning Developer who is passionate about solving hard problems with data, with strong software development skills.
- Design, implement, and optimize machine learning models for event analytics, text processing, personalisation;
- Analyze performance and event statistics data, present your findings to the team to help drive product decisions;
- Write production-ready Python code that is easy to support;
- Work with other engineers to launch and deploy new services.
- Strong experience in classification, information retrieval, natural language processing, machine learning and related sub-fields.
- Knowledge of machine learning theory and algorithms, both deep learning and classical like SVM, random forest, gradient boosting methods etc.
- Experience in processing large amounts of potentially dirty data.
- Proficiency with Python and common machine learning libraries.
- Good communication skills.
- Creative and critical thinking capabilities and troubleshooting skills.
- Work with minimal supervision, be proactive and self-motivated.
- BS or MS in Computer Science or related field (Deep Learning, Machine Learning, Data Mining, Natural Language Processing, Information Retrieval). PhD is a big plus.
Good to have:
- Proficiency in common machine learning/deep learning frameworks: sklearn, tensorflow, theano, pytorch, keras, etc.
- Experience with common NLP tasks: language recognition, stemming & morphological segmentation/parsing, text categorization, clustering, text summarisation etc.
- Knowledge of any foreign language is a definite asset. Most important are German, Spanish, Portuguese, French, Italian, Russian, Japanese, Chinese, Korean;
- Publications in related fields;
- Experience with Dask, Apache Spark, and similar software;
- Choose your own hardware when you join;
- Great insurance package;
- 15+ paid vacation days;
- Flexible hours — we track performance, not butts-in-seats.