Staff Engineer, Machine Learning & Data Infrastructure
About The Position
OKX is seeking an experienced engineer to lead the architecture and hands-on development of machine learning and data infrastructure supporting risk and fraud prevention systems. The role helps teams move models from experimentation into reliable production services, improves model execution performance, and builds scalable data foundations for training, evaluation, and real-time decision-making. The engineer will optimize inference latency, throughput, resource utilization, and cost; build reusable infrastructure for the model lifecycle; and design offline data pipelines using Databricks, Apache Spark, and warehouse or lakehouse technologies. The position also connects offline and online feature pipelines, evolves reliable feature services and data integrations, and sets technical direction while mentoring engineers and leading cross-functional delivery.
Requirements
- A record of building and operating large-scale distributed systems, ML infrastructure, or data platforms, with technical ownership across teams.
- Hands-on experience optimizing production model performance, supported by measurable improvements in latency, throughput, utilization, or cost.
- 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.
- Experience with the model lifecycle, including training/serving integration, validation, deployment, observability, and rollback.
- Strong Python and SQL skills, plus at least one backend or systems language such as Java, Scala, Go, or C++.
- Ability to turn ambiguous requirements into clear architecture and deliver maintainable systems through hands-on engineering and technical leadership.
Nice To Haves
- SageMaker, Alibaba Cloud PAI/EAS, or comparable ML platforms.
- Inference optimization using tools such as ONNX Runtime, TensorRT, or Triton.
- Delta Lake, MLflow, feature stores, Kafka, Flink, and low-latency data stores.
- Fraud detection or financial risk systems.
- Fluency in Mandarin.
Responsibilities
- Improve model inference latency, throughput, resource utilization, and cost by profiling the full prediction path and delivering measurable improvements without unacceptable model-quality regressions.
- Build reusable infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
- Design and optimize offline data pipelines on Databricks, Apache Spark, and warehouse/lakehouse technologies, including data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost.
- Connect offline and online feature pipelines, ensuring point-in-time correctness, consistent feature definitions, freshness, lineage, and reproducible backfills.
- Evolve reliable feature services and data integrations supporting risk models and decision systems.
- Set technical direction, mentor engineers, and lead delivery across ML, data engineering, cloud infrastructure, security, and product teams.
Benefits
- Competitive total compensation package.
- L&D programs for employees' growth and development.
- Team-building programs and company events.
- Wellness and meal allowances.
- Comprehensive healthcare schemes for employees and dependants.
- A performance bonus and long-term incentives may be provided as part of the compensation package.
- Medical, financial, and/or other benefits, dependent on the position offered.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
