- Improve model inference latency, throughput, resource utilization, and cost. Profile the prediction path—from feature retrieval and preprocessing to model execution—and implement 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.
- 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.
- 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, 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.
- 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
- Competitive total compensation package
- L&D programs for employees' growth and development
- Various team building programs and company events
- Wellness and meal allowances
- Comprehensive healthcare schemes for employees and dependants
- More that we love to tell you along the process!
#LI-SK1
#LI-HYBRID
Skills Required
- Experience building and operating distributed systems, ML infrastructure, or data platforms, including ownership of meaningful production components.
- Hands-on experience improving production model performance, including latency, throughput, resource utilization, or cost.
- Strong experience with Databricks and Apache Spark or comparable offline data platforms, including distributed query optimization, storage layouts, data correctness, and workload cost management.
- Experience with the machine learning 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 and deliver maintainable systems with limited guidance.
- Experience with SageMaker, Alibaba Cloud PAI/EAS, or comparable ML platforms.
- Experience with inference optimization using 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.
OKX Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about OKX and has not been reviewed or approved by OKX.
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Fair & Transparent Compensation — Pay is considered competitive or above market, especially in engineering, product, and legal roles across major hubs. This positioning is consistently cited as a major attraction for candidates.
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Healthcare Strength — Role descriptions indicate comprehensive medical, dental, vision, life, and disability coverage, with employer-paid premiums in some cases. Health coverage is highlighted alongside core benefits like PTO and parental leave.
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Wellbeing & Lifestyle Benefits — Allowances for education and fitness, meal perks and snacks, team-building budgets, and structured learning programs are described across locations. These extras enhance the total rewards package beyond base pay.
OKX Insights
What We Do
Founded in 2017, OKX is one of the world’s leading cryptocurrency spot and derivatives exchanges. OKX innovatively adopted blockchain technology to reshape the financial ecosystem by offering some of the most diverse and sophisticated products, solutions, and trading tools on the market. Trusted by more than 20 million users in over 180 regions globally, OKX strives to provide an engaging platform that empowers every individual to explore the world of crypto. In addition to its world-class DeFi exchange, OKX serves its users with OKX Insights, a research arm that is at the cutting edge of the latest trends in the cryptocurrency industry. With its extensive range of crypto products and services, and unwavering commitment to innovation, OKX’s vision is a world of financial access backed by blockchain and the power of decentralized finance.







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