Interested in joining the Deep Algo Team?



To interpret these algorithms, our technical challenges consist in massive parallel computing over huge graph networks.



What is a Deep Algo engineer like ?

She/He is a member of a small, talented and passionate team, who loves tackling big challenges.

She/He is enthusiastic not only about being involved in product decisions but also about implementing them and promoting a feedback loop with the users.

Deep Algo’s engineers are like entrepreneurs : they love autonomy, hard-working and simplicity to make the impossible feasable.








DevOps

What we have

The actual Deep Algo's codebase is managed by gitlab and gitlab CI/CD up to the deployment phase. The final product is deployed on AWS infra using Docker technology. Our micro-services architecture offers: web based front end (vue.js), several API Servers (Ruby On Rails, MongoDB, PostGresql, Elastic Search ...) running massive parallel jobs.

What we need

A devOps leader fully involved in the development and the architecture of the product.
A 100% automated vision by developing or using dedicated tools to insure an up to date environment and an “easy delivery” system.
A guru to promote how DevOps is vital to all members of the team.
A Plus: initiate a new architecture to handle Machine Learning and Deep Learning neural networks.







Software Engineer

What we have

Deep Algo’s platform is based on a microservices architecture. We already have a large tested code base handling graph networks algorithms: from reverse code engine to statistical graph clustering through dataflow analysis and NLP for search engine.

What we need

We believe that the best code is no code at all: be simple is key! Revamping is a daily task for us.
We’re facing many challenges like increasing language coverage with tests, improving DAO performances, optimizing parallelisation of our processes and microservices, or working on our indexing elastic search engine.
A plus: mathematical knowledge is always appreciated for good communication between R&D and IT







Data Science

What we have

Unsupervised statistical network clustering, labelized database for programming language reverse engineering and supervised learning. We can make this database increase almost at will to insure a proper supervised learning. We successfully realized PoC using DL to extract features with unsupervised auto-encoder.

What we need

Energized engineers ready to start a project from scratch.
Genius ready to tackle one of the biggest scientific challenges: make algorithms understandable by anyone.
Explorers fascinated by the idea of using ML and DL in graph mining.