We are seeking a highly skilled and experienced Staff Software Engineer to join our MLE-Backend group. The ideal candidate will have a strong background in distributed systems, data engineering and software engineering, and will be responsible for leading the design, development, and deployment of top quality data pipelines, data lake and an ML feature platform.
What you'll be doing
System Design and Architecture: Develop and maintain robust, scalable, and efficient distributed systems and data pipelines to help build the new generation of ironSource's ML feature platform.
Research and Innovation: Stay current with the latest advancements in machine learning data pipelines, feature stores, and apply innovative techniques to improve data aggregations, monitoring, cleaning & filtering and preparing for ML training.
Cross-Functional Collaboration: Work closely with data scientists, software engineers and product managers to build the data lakes and data pipelines.
What you'll be doing
System Design and Architecture: Develop and maintain robust, scalable, and efficient distributed systems and data pipelines to help build the new generation of ironSource's ML feature platform.
Research and Innovation: Stay current with the latest advancements in machine learning data pipelines, feature stores, and apply innovative techniques to improve data aggregations, monitoring, cleaning & filtering and preparing for ML training.
Cross-Functional Collaboration: Work closely with data scientists, software engineers and product managers to build the data lakes and data pipelines.
Requirements:
Proficiency in programming languages such as Scala/Java.
Hands on experience in streaming technologies (Kafka, Kinesis, SQS).
Expert in big data processing frameworks such as Apache Spark, Trino, Flink.
Data Lake management knowledge: table formats (Iceberg, Delta). Data-warehouses (Redshift, Big Query, Snowflake)
MLOps systems – Model registry, Experiment Tracking (MLFlow, W&B), Feature Store management (Feast, Tecton), Workflow management (Kubeflow, Airflow)..
Knowledge of microservices architecture and event-driven design.
You might also have:
experience with ML frameworks such as TensorFlow, PyTorch.
experience with high scale distributed systems.
Proficiency with cloud platforms such as AWS, and experience with containerization technologies like Docker and Kubernetes.
Proficiency in programming languages such as Scala/Java.
Hands on experience in streaming technologies (Kafka, Kinesis, SQS).
Expert in big data processing frameworks such as Apache Spark, Trino, Flink.
Data Lake management knowledge: table formats (Iceberg, Delta). Data-warehouses (Redshift, Big Query, Snowflake)
MLOps systems – Model registry, Experiment Tracking (MLFlow, W&B), Feature Store management (Feast, Tecton), Workflow management (Kubeflow, Airflow)..
Knowledge of microservices architecture and event-driven design.
You might also have:
experience with ML frameworks such as TensorFlow, PyTorch.
experience with high scale distributed systems.
Proficiency with cloud platforms such as AWS, and experience with containerization technologies like Docker and Kubernetes.
This position is open to all candidates.
