At TrademarkNow we build the world’s leading trademark search and protection tools using the latest Machine Learning, AI and NLP technologies and techniques to imitate how a real lawyer would assess Trademark Likelihood of Confusion. While complex at core, we hide the AI-nerdy stuff behind a simple – yet polished – User Interface used by some of the biggest brand owners in the world.

With us you get to work with a technology stack that utilizes the best programming tools and practices, such as Functional Programming, Microservice based Architecture and an Agile way of working. We are modern and our systems are modern with no legacy crap.

You will also get to work with Big Data. Our Information Retrieval cluster executes complex linguistic similarity searches from a total of 1 TB of in-memory trademark data using custom Data Structure and Algorithms.

Our company is growing fast and currently we are looking for a hands-on Natural-Language-Processing (NLP) oriented Software Engineer / Data Scientist to take responsibility for the design and development of our textual analysis components, such as morpheme segmentation, statistical language models, semantic similarity, etc.

We appreciate especially the following skills:

• a solid understanding of modern computational linguistics and natural-language processing.
• M.Sc or Ph.D. in CS with machine learning, computational linguistics or NLP
• experience with scripting languages, shell scripts and SQL
• experience with Scala and/or Java and working with biggish data volumes
• industry experience preferred, but not mandatory
• eager to learn new things 

As part of the TrademarkNow team you will be:

• trusted to work in the best way possible and have flexible working hours
• given all the tools you need to do your work
• given a lot of responsibility
• learning new things and having an impact on our product 

If you are interested in joining our team, please send your CV/LinkedIn profile to our Talent & People Manager Katariina Hava (katariina.hava@trademarknow.com). We will read applications as they come and will start interviewing as soon as we find potential candidates.