Sr. Software Engineer, Machine Learning & Data Infrastructure
About The Position
OKX is seeking a seasoned engineer to build and improve the machine learning and data infrastructure supporting its risk and fraud prevention systems. The role involves moving models from experimentation into reliable production services, improving model execution performance, and developing data pipelines for training, evaluation, and real-time decisions. The engineer will own projects and operational issues, identify improvements proactively, and deliver with limited guidance. The position works across model inference optimization, model lifecycle infrastructure, offline and online feature pipelines, and data platforms. It collaborates with machine learning, data engineering, cloud infrastructure, security, and product teams, and contributes to technical discussions, code reviews, and knowledge sharing.
Requirements
- Experience building and operating distributed systems, ML infrastructure, or data platforms, including ownership of meaningful production components.
- Hands-on experience improving production model performance, such as latency, throughput, resource utilization, or cost.
- Strong experience with 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 and 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 work independently through loosely defined problems, make sound implementation decisions, and deliver maintainable systems with limited guidance.
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 by profiling prediction paths and implementing measurable improvements while preserving model quality.
- Build and maintain infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback across cloud platforms and regions.
- Develop and optimize offline data pipelines using Databricks, Apache Spark, and warehouse or lakehouse technologies.
- Address data modeling, partitioning, joins, shuffles, skew, incremental processing, and cost in data pipelines.
- Work on offline and online feature pipelines, supporting point-in-time correctness, consistent feature definitions, freshness, lineage, and reproducible backfills.
- Build and maintain reliable feature services and data integrations for risk models and decision systems.
- Take ownership of projects and operational issues, communicate progress and tradeoffs, and collaborate with ML, data engineering, cloud infrastructure, security, and product teams.
- Contribute to technical discussions, share knowledge, and help teammates through code reviews and collaboration.
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.
- A full range of 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
