OKX

Staff Engineer, Machine Learning & Data Infrastructure

San Jose, California, United States

Actively hiring, first seen by us today

Pay: $214,666–321,999/yearAnnual period assumed. Location-specific pay — pay source
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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Work setting: Not stated
Employment: Not stated

What you’ll work on

Full posting

Improve model inference latency, throughput, utilization and cost
Build reusable infrastructure for model deployment, serving, monitoring and rollback
Design and optimize offline data pipelines on Databricks and Spark
Connect offline and online feature pipelines with point-in-time correctness
Set technical direction and mentor engineers across ML, data and cloud teams

From the employer’s posting

Improve model inference latency, throughput, resource utilization, and cost.

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

Connect offline and online feature pipelines, ensuring point-in-time correctness

Set technical direction, mentor engineers, and lead delivery across ML, data engineering, cloud infrastructure, security, and product teams.

About OKX

At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom.

In the employer’s words · Read in context

Apply at OKX

Posting record

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Actively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.

Status in our records: Live
First seen by us: Nov 4, 2026today
Recorded sightings: 1

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$118K–$193K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 3,721 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 0

Early. Similar jobs usually come down between day 7 and day 55.

From 9,971 similar postings that came down. Coming down is not always a hire.

OKX is hiring

15 open jobs

Open jobs by the week we first saw them, last 12 weeks.

Hiring is picking up
Takes jobs down at a usual pace

Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

C++
Java
Python
Scala
SQL
Databricks
Delta Lake
Kafka
MLflow
SageMaker
Spark
Go

Where the posting names them

- 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.

- 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.

- 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.

- 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.

Nice to Haves - SageMaker, Alibaba Cloud PAI/EAS, or comparable ML platforms. - Inference optimization using tools such as ONNX Runtime, TensorRT, or Triton.

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Job description

View original posting ↗

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 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
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 $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.

#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.

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