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OKX · job-boards.greenhouse.io · checked today

Staff Engineer, Machine Learning & Data Infrastructure

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets.

Skills, with evidence

• Data modelling
  Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.must havewrong?
• Incremental loads
  Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.must havewrong?
• Python
  Strong Python, SQL, and at least one backend or systems language such as Java, Scala, Go, or C++.must havewrong?
• SQL
  Strong Python, SQL, and at least one backend or systems language such as Java, Scala, Go, or C++.must havewrong?
• Spark
  Deep understanding of Databricks and Spark, or comparable offline data platforms, including execution plans, distributed query optimization, storage layouts, data correctness, and workload cost management.must havewrong?
• AWS
  Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.must have · not practised herewrong?
• Azure
  Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.must have · not practised herewrong?
• GCP
  Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.must have · not practised herewrong?
• Cost & performance
  Improve model inference latency, throughput, resource utilization, and cost.wrong?
• Streaming
  You will help teams move models from experimentation into reliable production services, improve model execution performance, and build scalable data foundations for training, evaluation, and real-time decisions.wrong?
• Data quality
  Experience with the model lifecycle, including training/serving integration, validation, deployment, observability, and rollback.wrong?
• Failure handling
  Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.wrong?
• Idempotency & backfills
  Connect offline and online feature pipelines, ensuring point-in-time correctness, consistent feature definitions, freshness, lineage, and reproducible backfills.wrong?
• Warehousing
  Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, addressing data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.wrong?