Sr. Software Engineer, Machine Learning & Data Infrastructure

OKX • San Jose, CA

18h • $178,888 - $268,333 • Hybrid

About The Position

OKX is seeking a seasoned engineer to build and improve the machine learning and data infrastructure behind risk and fraud prevention systems. The role focuses on moving models from experimentation into reliable production services, improving model execution performance, and developing data pipelines that support training, evaluation, and real-time decisions. The engineer will own projects and operational issues, proactively identify improvements, and deliver with limited guidance while collaborating across machine learning, data engineering, cloud infrastructure, security, and product teams.

Requirements

• Experience building and operating distributed systems, ML infrastructure, or data platforms, with 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.
• 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 while preserving model quality.
• Profile prediction paths, including feature retrieval, preprocessing, and model execution, and implement measurable performance improvements.
• 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 pipeline cost.
• 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 support teammates through code reviews and collaboration.

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, along with medical, financial, and/or other benefits depending on the position offered.

What This Job Offers

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