Summary
Who we are
We are pioneering the development of artificial intelligence and other technologies to augment and enhance the human ability to invent. Working with some of the world’s best-known technology companies in North America, Europe, and Japan, our technology enables the products of tomorrow to be invented faster and more efficiently than has previously been possible. Already, all 10 of the world’s largest tech companies including Amazon, Apple and Google reference granted patents based on our inventions.
Objective
Iprova’s technology enables the creation of inventions by identifying and connecting useful inventive signals from day-day social, market and technological advances – even if they are very distant to the target invention area. The overall objective of the project is to use the latest developments in Machine Learning (ML) and Natural Language Processing (NLP) to research and build software prototypes for new features to add to our solution.
Scope
The successful candidate would work with Iprova’s R&D team to bring to life a few features from our product roadmap. These features pertain to different ways of automatically mining large text corpora for information and ideas, to assist human inventors in their work and help them become more performant.
The following are, broadly speaking, the technical areas within ML and NLP that would be relevant to the project:
- Text classification
- Semi-supervised learning and automated text labeling
- Information retrieval
- Knowledge graph construction and mining
- The candidate would need to build a working prototype of these features that interfaces with our existing technological solution and data store, and design and implement user studies to test the efficacy of their solution.
- Skills/Experience Required
- An undergraduate degree in Computer Science, Maths, Statistics, or similar
- Good knowledge of at least 2-3 of the sub-areas of NLP that have been mentioned in the scope above. You may have acquired this knowledge through your coursework or through other means, but you should be able to demonstrate your knowledge in these areas
- Good knowledge of Python, common data science libraries like numpy and pandas, and deep learning frameworks like Pytorch or Tensorflow. You may use a non-Python stack to do your R&D work, but you should be prepared to port some of it to our Python-based systems.
- A genuine interest and competence in this area of study as demonstrated by research papers, side projects etc. Whilst this is not essential for the role, we do look for candidates who show curiosity for the subject beyond an academic setting.
- Excellent communication skills in English.
What you will gain from this project
A chance to:
- Read and get familiar with the latest research in ML and NLP.
- Apply your technical skills on the latest tools and frameworks in the above technologies, or introduce a new framework or tool to us that we should be using in our work. We are happy to take your lead on this.
- Publish your work. Our team has previously published at top conferences like the International Conference on Learning Representations (ICLR).
- Understand and experience how ML is being applied in a real world problem setting.
- Interface with production-grade software development for ML applications, including various themes in MLOps.
- Practise your project management and communication skills
- Make a valuable contribution to Iprova as well as develop your skills in the commercial world.
Application
If you are interested in this position please send us your CV and a letter, detailing why you believe you will be best suited for this project to our email address recruitment@iprova.com. We look forward to hearing from you.
Please note
During periods where we receive very high numbers of applications, we will only reply to those we wish to advance.
We will only use the information you give us for recruitment purposes. By submitting your details to apply for a position, you consent to have your personal details used to process the job application. We will treat the information in strict confidence.
