benshi.ai

Data Infrastructure Engineer

We are looking for engineers to join our data infrastructure team. We are interested in individuals with a strong engineering background, and who are knowledgeable about building large scale data systems. You will have the rare opportunity of building resilient and scalable machine learning products from scratch, together with other talented engineers, to boost global health via cutting-edge ML algorithms.

Job detail

If you are passionate about building robust scalable data systems, and are excited about using your skills to improve the world, we want to meet you!

Responsibilities

  • Working with the engineering team to design and build the data infrastructure required to support the operations of our machine learning platform. This involves working in data ingestion, data processing, model productionalization, and related operational services
  • Working with data scientists to create and improve data science workflows, i.e. machine learning lifecycle management

Minimum qualifications

  • BSc/BEng degree in computer science, mathematics, physics, electrical engineering, machine learning or related fields; or equivalent technical proficiency
  • Solid coding skills in at least two of the following languages: Go, Java, Scala, C/C++, Python
  • Highly skilled in any of the following data processing tools: SQL, Apache Spark, Kafka, Hive, or similar tools

Preferred qualifications

  • Experience building early stage robust and scalable products
  • Strong background in computer science: algorithms, data structures and computer systems
  • Experience with continuous deployment of models with build pipeline
  • Experience with Spark performance tuning, and data pipeline testing
  • Experience with Cassandra, HBase, or similar scalable K/V Stores
  • Experience with cloud services (GCP, AWS or Azure)
  • Experience with scheduling tools (Airflow, prefect etc)
  • Experience with machine learning lifecycle management tools (e.g. mlflow)
  • Best practices advocate (tests, documentation...)