Staff Engineer, Machine Learning & Data Infrastructure

OKX • San Jose, CA

12h • Hybrid

About The Position

OKX is seeking an experienced engineer to lead the architecture and hands-on development of the machine learning and data infrastructure supporting its risk and fraud prevention systems. The role helps 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.

The engineer will optimize model inference and offline data pipelines; build reusable infrastructure for the model lifecycle; connect offline and online feature pipelines; and evolve reliable feature services and data integrations. The role also sets technical direction, mentors engineers, and leads delivery across machine learning, data engineering, cloud infrastructure, security, and product teams.

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 proficiency in 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

• Experience with SageMaker, Alibaba Cloud PAI/EAS, or comparable ML platforms.
• Inference optimization using tools such as ONNX Runtime, TensorRT, or Triton.
• Experience with Delta Lake, MLflow, feature stores, Kafka, Flink, and low-latency data stores.
• Experience with fraud detection or financial risk systems.
• Fluency in Mandarin.

Responsibilities

• Improve model inference latency, throughput, resource utilization, and cost.
• Profile the full prediction path, from feature retrieval and preprocessing through model execution, and deliver 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 or lakehouse technologies, addressing 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.
• Learning and development programs for employee 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed