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ML/Data Infrastructure Engineer

Toronto or San Mateo


Looking for a ​seasoned ML​ infrastructure engineer who wants to build the next generation AI pipelines and API’s.​ ​If you are comfortable to swim in Petabytes of data to explore trends and insights and to write scalable code for event processing and ML data pipelines, come talk to us.


  • Designing high-performance RESTful web services serving terabytes of data per day.

  • Responsible for designing, developing, and implementing our backend and machine learning data infrastructure -- including event-based data ingestions, stream processing, data warehouse, data pipelines, visualization, analytics and applications

  • Develop and maintain distributed data pipelines for ETL, feature engineering, and model running

  • End-to-end data processing, troubleshooting, and problem diagnosis, performance benchmark, load balance, and code reviews.

  • Experience on data management platform (DMP) is a plus



  • 5+ years of infrastructure building. Deep knowledge of Python and Elixir is a big plus

  • 4+ years of experience designing and implementing large, scalable API for over billions of requests per day.

  • 3+ years of experience with big data technologies such as Spark, Kafka, Airflow, Druid

  • 1+ years of experience with data warehouse products such as Redshift and Snowflake.

  • Production experience on Druid, ElasticSearch, Kafka, AWS Lamba, AWS SageMaker a big plus.

  • Production experience on docker/Kubernetes a big plus.

  • Good understanding of computer algorithms

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