Sr Machine Learning Engineer, Compliance at OKX

San Jose, California, United States -

Full Time

Start Date

Immediate

Expiry Date

28 Jun, 26

Salary

268333.0

Posted On

30 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning Engineering, Compliance, MLOps, Feature Pipelines, Model Deployment, Monitoring, Drift Detection, Retraining Workflows, Python, ML Frameworks, CI/CD, Spark, Databricks, LLM Tooling, Anomaly Detection, Explainability Frameworks

Industry

Financial Services

Description

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. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity Building ML systems for compliance at a crypto exchange is a different kind of problem from most ML engineering work. The data spans on-chain transactions, fiat flows, KYC records, and behavioural signals that very few organisations have in one place. The problems are genuinely unsolved, the stakes are high, and the work has direct bearing on how a global exchange detects and responds to financial crime. For someone who wants their engineering work to matter beyond model accuracy metrics, this is an interesting place to be. This role sits within a team of data scientists, analytics engineers, and compliance specialists who are building the analytical and AI infrastructure that powers the compliance function. You will work across the full ML lifecycle, from feature pipelines and model development through to deployment and monitoring, with close involvement from the domain experts who understand what the models need to do in practice. AI-assisted development is how this team works. LLM-assisted coding, automated analytical pipelines, and AI-powered investigation tooling are part of the daily workflow. We are looking for engineers who already operate this way and who can raise the bar for what that looks like in a production compliance environment. What You’ll Be Doing Design, build, and deploy ML models for compliance use cases including AML transaction monitoring, customer risk rating, KYC/KYB risk scoring, sanctions exposure detection, and SAR analytics, working closely with data scientists on model architecture and with data engineers on pipeline design. Own the production infrastructure for compliance ML: feature pipelines, model serving, monitoring, drift detection, and retraining workflows. Models in a compliance context need to be reliable, auditable, and well documented, and you will be responsible for making sure they are. Build and maintain internal ML tooling that the broader team depends on: reusable pipeline components, experiment tracking, model registries, and evaluation frameworks that raise the quality and speed of model development across the team. Apply AI-assisted coding and automation as a matter of course: using LLM tooling to accelerate development, building automated pipelines that reduce manual analytical work, and integrating LLM-based capabilities into compliance workflows such as SAR narrative assistance, alert summarisation, and investigative triage. The expectation is that you bring this fluency with you, not that you develop it here. Work with data scientists to take research-stage models into production, reviewing feature logic, validating pipeline assumptions, and bridging the gap between a notebook and a deployment that a compliance team can rely on. Collaborate with compliance and legal stakeholders to ensure models are explainable and documented to the standard required for internal governance and regulatory review. Keep a close eye on developments in compliance-relevant ML: graph neural networks for network-based AML detection, anomaly detection approaches for novel typologies, and emerging LLM applications in regulated environments, bringing relevant ideas into the team's work where they hold up to scrutiny. What We Look For In You 8+ years in ML engineering, data science, or a closely related field, with a strong track record of taking models from prototype to production in environments where reliability and auditability matter. We welcome candidates across seniority levels; scope will be calibrated to your experience. Solid Python and experience with ML frameworks alongside hands-on MLOps practice including model deployment, monitoring, and CI/CD pipelines for ML workflows. Experience with big data platforms such as Spark, Databricks, Hadoop, or MaxCompute, and comfort designing and optimising both batch and real-time data pipelines that feed production ML systems. Demonstrable fluency with AI-assisted development: you use LLM coding tools before, you have built LLM-integrated pipelines or automation workflows in a professional context, and you have a clear, practical view on where these tools genuinely improve engineering output and where they introduce risk. A good working knowledge of ML fundamentals across supervised, unsupervised, and anomaly detection methods, and an understanding of how different approaches translate to compliance problem formulations. Familiarity with explainability frameworks such as SHAP or LIME, and an appreciation for what model governance and auditability require in a regulated environment. Good communication skills and a collaborative working style, with the ability to work effectively alongside data scientists, compliance domain experts, and engineers who each bring a different perspective to the same problem. Experience in financial services, fintech, or a crypto exchange, particularly in AML, KYC/KYB, transaction monitoring, or a related compliance domain, is a meaningful advantage. Familiarity with the crypto ecosystem, on-chain data, blockchain analytics, or VASP regulatory frameworks is a plus and will give you a head start in understanding the data you will be working with. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy 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 $223,611.00 to $268,333.00. 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-HYBRID#LI-HYBRID #LI-ED1 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.

Responsibilities

Design, build, and deploy Machine Learning models for compliance use cases such as AML transaction monitoring, risk scoring, and sanctions detection, while owning the production infrastructure including feature pipelines and model serving. Responsibilities also include building internal ML tooling, applying AI-assisted coding, and ensuring models are explainable and documented for regulatory review.