Build and operate machine learning and data infrastructure for risk and fraud prevention systems. Improve model inference performance, deploy and monitor models across cloud platforms, and develop offline and online feature pipelines. Optimize Databricks and Spark data processing, ensure feature correctness and reproducibility, and maintain reliable feature services. Own projects and operational issues while collaborating with ML, data engineering, cloud infrastructure, security, and product teams.
Who We AreWhat You’ll Be Doing What We Look For In You Nice to Haves Perks & Benefits
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.
About the OpportunityWe are looking for a seasoned engineer to build and improve the ML and data infrastructure behind our risk and fraud prevention systems. You will work hands-on to move models from experimentation into reliable production services, improve model execution performance, and develop data pipelines that support training, evaluation, and real-time decisions. You will take ownership of your work, identify improvements proactively, and deliver with limited guidance
- 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!
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 $178,888 to $268,333
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.
#LI-SK1
#LI-HYBRID
#LI-SK1
#LI-HYBRID
Notice:
All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website.
Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX's Candidate Privacy Notice.
Similar Jobs
Automotive
Build and productionize large-scale data pipelines and platforms to generate ML and evaluation datasets from driving logs and simulation data. Design tools for data mining, exploration, and analysis, and collaborate with Data Science and Quantitative Analytics to deliver high-impact, production-ready infrastructure and dataset generation.
Top Skills:
A/B Experiment InfrastructureC++Distributed SystemsPythonSQL
Artificial Intelligence • Fintech • Insurance • Marketing Tech • Software • Analytics
Investigates and processes moderately complex to complex workers compensation claims, determining coverage, liability, compensability, reserves, damages, and settlements. The role coordinates litigation, engages experts, conducts negotiations and field investigations, and supports arbitrations and mediations. It also provides technical guidance, mentors team members, assists with caseload management, and participates in quality reviews. Candidates need advanced claims-handling and insurance coverage knowledge, strong negotiation and investigation skills, applicable licenses, and typically a bachelor’s degree with 3–5 years of related experience.
Consumer Web • eCommerce • Information Technology • Retail • Software • Analytics • App development
Provides fast, friendly checkout service; operates registers and self-checkout areas; scans and bags merchandise; processes payments and exchanges; answers customer questions; maintains a clean, safe workstation; and assists with stocking, carts, and other store duties as needed.
Top Skills:
Cash RegisterComputerSelf-Checkout Systems
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
