Staff Engineer, Machine Learning & Data Infrastructure

5 дн. назад

СШАLead

machine learningdata infrastructuremodel deploymentmodel inferenceprofilingfeature retrieval

Lead architecture and development of ML and data infrastructure for risk and fraud prevention systems in a crypto exchange.

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er.

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.
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.

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.

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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