As part of the strengthening of the R&D, we are looking for a Senior Machine Learning Engineer. Under the direction of the Head of Machine Learning, he/she will be performing the following tasks:

• Lead the development of our new Machine Learning platform in Kubernetes:

  • TFX training pipeline using Kubeflow
  • Web Service for inference and pipeline management
• Coach the junior Data Engineers, ML Engineers and Data Scientists to improve their coding, architectural and design skills.
• Deploy Machine Learning models for our clients’ use-cases.
• (Depending on the candidate willingness) Help steering the Machine Learning product/service.

Graduated from an Engineering School (Grande Ecole d’Ingénieur) or from a STEM course from a top-tier University (bac+5 / Master), you have at least 5 years of experience on similar functions. Finally, you share our values - Honesty, Team Spirit, Excellence, Proactivity and Grit. You also possess the following skills:

Mandatory:

• Production-level knowledge of Python (typechecks, tests, lints, package management, web servers, data serialization and machine learning open-source libraries, etc.).
• Good knowledge of Docker.
• Previous experience managing web services (REST APIs and SQL databases).
• Good grasp of Machine Learning basics: training vs. testing vs. prediction, models in production, overfitting, hyperparameter tuning, etc.
• Good grasp of computer science basics: I/O, memory, processes/threads, networking, etc.
• You are rigorous, autonomous and proactive.

Very nice-to-have:

• Good knowledge of distributed processing engines such as Spark or Flink (or runtimes such as Apache Beam).
• Good knowledge of distributed filesystems such as HDFS.
• Kubernetes knowledge.
• Monitoring and logging tools knowledge (such as Prometheus, Loki, Grafana).
• Tensorflow knowledge.
• TFX and Apache Beam knowledge.
• Kubeflow knowledge.

If you profile matches, we will contact you for a first round of interviews to validate the alignment between your skills and mindset and our offer.

We have the following steps of interview:

• 1h Phone interview to check background and past experiences, motivation and explain the job in detail.
• 1h30 Technical interview with a Data Scientist and a Data Architect.
• 1h Interview with the Head of Machine Learning.
• (possible) Interview with company CTO.