Please note: This is an on-site position in Copenhagen, Denmark. Applications for remote work will not be considered.
At Relink we are matching people to jobs and companies with new hires.
This is similar to a recommendation engine, but you can not just slap some collaborative filtering on it and call it a day. Using historic public and private data, we apply unsupervised learning and graph modeling to find latent properties of jobs and people, allowing our matching capabilities to go beyond just the CV.
We are looking for someone who can tackle problems within NLP, graph modeling, deep learning (for NLP), unsupervised and supervised machine learning. Experience within any of these areas is a plus, but neither is required.
You should be resourceful: you will not always have access to a perfect training set for a problem, and will have to conjure up ways to create it.
It is important that you understand the practicalities of the real world and that you can work, or learn to work with, the newest of technologies and methods within the machine learning and deep learning space. At the same time, you ought to know how your work is applicable in production. As your work will be running in production a solid software development discipline is needed.
Candidates preferably hold a Masters or PhD degree with practical exposure to deep learning and NLP.
Perhaps you have published scientific work within machine learning or similar disciplines? We would love to see that too.
We want you to:
Think and develop at scale in order to handle datasets of hundreds of millions of profiles.
Optimise not by shaving off percentage points, but by rethinking the problem.
Work closely with our data engineers and developers to ensure elegant, modular integrations in production.
Follow the NLP and AI / ML research.
Assist with experimental design.
Have an honest feedback loop about what is working and what is not.
Experience with the following technologies is a major plus:
Deep Learning for NLP and associated frameworks (e.g., TensorFlow, Keras, Torch, etc.)
Scala and/or functional programming
Python and/or R
Spark and Spark Streaming
TDD and BDD
Containerization (docker, etc.)