TalentSeed is revolutionizing the job market since 2017, through a specialized recruitment and selection service supported by our innovative technological Talent Management platforms.

We are an entity registered with the Portuguese board of psychologists and our mission is to present solutions in the Human Resources field, resorting to technology as a foundation of the innovation that we seek to present to our partners.

Our young, fun, and dedicated team aims to add value to companies and candidates, striving for the excellence of our services!

TalentSeed is currently recruiting:

Machine Learning Engineer - Cloud (Toronto, Vilnius or Antwerp) (M/F)

CHALLENGE

Our client is a data science company turning IOT sensor data into rich insights about people’s behaviour and real-time context.

As a MLE, you will be integrated into a production environment, within a team with a passion for machine learning and data science. You’ll work in a fast-paced environment where you can integrate and optimize machine learning pipelines, and work closely with the data science team and data engineering team.

You will have the following responsibilities:

• Design and implement methods for both supervised and unsupervised learning;
• Working with GPS fixes and sensors; training algorithms using this big data to predict low events or concepts and human behaviour;
• Improving the development of machine learning pipelines by creating tools;
• And also improving current methods and models into a production environment, from a performance and architecture point of view.

WE ARE LOOKING FOR

• Master degree in Computer Science or a similar field with fluency in English (mandatory);
• Understanding of machine learning concepts such as cross-validation, clustering, supervised vs. unsupervised learning;
• Academic or Work experience in software engineering;
• Implementation and deployment of ML pipelines in a production environment;
• Fluency in Python and Java;
• Knowledge of big data engineering for development (e.g. Spark);
• Ability to critically interpret output quality based on quantitative analysis and tooling.

Bonus

• Expertise in data engineering;
• Experience with sensor data modelling from mobile devices or wearables;
• Know-how of stream processing (Kafka);
• Mobile development constraints and peculiarities learning.