Staff Engineer, Machine Learning & Data Infrastructure

About the Opportunity

We are looking for an experienced engineer to lead the architecture and hands-on development of the ML and data infrastructure behind our risk and fraud prevention systems. You will help 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.

What You’ll Be Doing

Improve model inference latency, throughput, resource utilization, and cost. Profile the full prediction path —from feature retrieval and preprocessing to 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/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.

What We Look For In You

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, SQL, and 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

Perks & Benefits

Competitive total compensation package

L&D programs for employees' growth and development

Various team building programs and company events

Wellness and meal allowances

OKX Statement:

OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

The salary range for this position is $214,666 to 321,999

The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via OKX internal or external careers site.

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