About the Role

From the Data Engineering team, you will work on ambitious and critical projects for the company such as:

• Our data access service

• Our ML inference service

• Data pipelines including ML pipeline, cold and live data ingestions

You will occupy a central position in the model development lifecycle, writing high-quality production code for our inference server, aligning closely with the product and platform teams and designing automated evaluation pipelines to continuously assess and improve performance.

You will be responsible for building scalable pipeline and help reduce build time for new clients.

Qualifications

Required qualifications:

• 3+ years of experience in data engineering

• Experience in dealing with the machine learning model lifecycle, ideally, the candidate has experience on serving machine learning models

• Strong background in data orchestration (Airflow or Dagster)

• Excellent communication and documentation skills

• Fluent English (French is not required)

• Python, Airflow, Docker

Nice to have:

• Experience in managing REST APIs

• Ray, Dagster, Github Actions, K8S, Python ML and scientific libraries (pytorch, numpy, sklearn, pandas)

• Experience in healthcare or insurance ML applications

• Apache Spark, multithreaded, parallel and distributed calculation experience

• Fit interview (30min): a first discussion to tell you more about the company and understand your background

• Live coding interview (45-60min): Check your programming skills around an algorithmic exercise

• System Design Interview (45-60min): Assess your ability to design scalable and maintainable ML pipelines