Sr. Software Engineer, Machine Learning & Data Infrastructure

Okx
San Jose, CA
On-site

Who this role is best for

Strong fit for engineers who build and optimize ML and data infrastructure in financial systems, with deep expertise in distributed systems and cloud platforms.

Best fit for

  • Candidates with strong experience in distributed systems and ML infrastructure in financial or risk domains
    — “build and improve the ML and data infrastructure behind our risk and fraud prevention systems”
  • Individuals who have taken ownership of production systems and delivered measurable performance improvements
    — “take ownership of projects and operational issues, communicate progress and tradeoffs”
  • Proficient in Python, SQL, and systems languages, with a track record in Spark and Databricks
    — “Strong Python and SQL skills, plus proficiency in at least one backend or systems language”

Things to consider

  • Candidates should be prepared to work across multiple teams including security and product
    — “collaborate with ML, data engineering, cloud infrastructure, security, and product teams”

How to stand out

  • Highlight experience with model lifecycle management and deployment in your resume and interviews
    — “Experience with the model lifecycle, including training and serving integration, validation, deployment, observability, and rollback”
  • Emphasize concrete results from optimizing model inference latency and cost
    — “Improve model inference latency, throughput, resource utilization, and cost”
  • Demonstrate expertise in building and maintaining feature services and data pipelines
    — “Build and maintain reliable feature services and data integrations for risk models and decision systems”
  • Showcase your ability to independently drive projects with minimal supervision
    — “deliver with limited guidance”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Improving model inference latency, throughput, resource utilization, and cost
  • Building and maintaining infrastructure for model packaging, versioning, deployment, serving, monitoring, and rollback
Typical background
Experience building and operating distributed systemsHands-on experience improving production model performance

Skills & requirements

Required

DatabricksApache SparkModel PackagingVersioningDeploymentMonitoringRollbackFeature PipelinesData ModelingPartitioningJoinsShufflesSkewIncremental ProcessingCost ManagementFeature ServicesData IntegrationsRisk ModelsDecision Systems

Preferred

SagemakerAlibaba Cloud Pai/easONNX RuntimeTensorrtTritonDelta LakeMlflowFeature StoresKafkaFlink

About the role

Original posting from Okx via Greenhouse

Who We Are

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 Opportunity

We 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

What You’ll Be Doing

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.

What We Look For In You

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.

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

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.

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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.

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Source: Okx careers (Greenhouse)

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