Sr ML Ops Engineer

• Design build and maintain scalable machine learning infrastructure - including feature stores model registries and deployment pipelines - to enable efficient model experimentation and productionization

• Implement end-to-end MLOps practices for automated continuous training testing deployment versioning of workflows and monitoring of ML models using modern frameworks (e.g. MLflow SageMaker Databricks Kubeflow)

• Build and maintain scalable ML pipelines for data ingestion feature engineering training and deployment ensuring reliability observability and compliance with LendingClubs data security policies

• Collaborate closely with data scientists to transition prototypes into robust production-grade solutions - ensuring reproducibility performance optimization and version control

• Build standards tools and libraries to streamline model lifecycle operations (training evaluation deployment) for other teams across LendingClub

• Create and maintain monitoring systems for model performance data drift and feature quality - with automated alerting and retraining triggers

• Ensure compliance and governance of ML assets through audit logging explainability tooling (SHAP LIME feature attribution) and model documentation practices that align with regulatory standards (e.g. Fair Lending FCRA)

• Integrate ML systems with business applications APIs and microservices to deliver real-time intelligence to customer-facing and internal decisioning systems

• Collaborate with platform DevOps and security teams to establish standardized infrastructure CI/CD pipelines and deployment environments for ML workloads

• Drive adoption of Responsible AI principles - ensuring fairness interpretability and transparency in models used for credit risk and customer decisions

• Automate evidence packaging for internal audits and regulatory reviews including data lineage test results and approval history

• Conduct performance benchmarking and cost optimization for ML workloads across cloud and compute resources

• 6 years of experience in Machine Learning Engineering Data Engineering or MLOps
• Bachelors degree or higher in a related field; or equivalent work experience
• Strong foundation in ML engineering with experience building and scaling pipelines and platforms in production
• Proficient in Python and experienced with data transformation and orchestration tools such as dbt (for modeling) Dagster (for orchestration) and Databricks (for large-scale data and model workflows)
• Deep understanding of the end-to-end ML lifecycle from data ingestion and feature engineering to model deployment and monitoring
• Hands-on experience or interest in data quality and observability frameworks such as Elementary and you appreciate how data reliability underpins model performance
• Experience implementing or supporting MLOps practices using frameworks like MLflow SageMaker or similar tools within Databricks or AWS
• Comfortable working in a cloud-native environment (AWS preferred) and using infrastructure-as-code (Terraform CloudFormation) to automate deployments
• Strong collaboration skills and the ability to partner with data engineers data scientists and product teams to make ML accessible reliable and compliant across domains
• You thrive in a FinTech environment balancing innovation with rigor around governance explainability and model monitoring.

Nice to have skills:

• Experience with LLM integration (RAG pipelines vector databases)
• Exposure to risk modeling credit scoring or fraud detection systems
• Familiarity with FinTech data privacy laws and model documentation standards
• Contributions to open-source ML infrastructure or platform components

Work Location
San Francisco

The above locations are eligible offices for this locations have been determined to foster in-person collaboration with this roles team or the related business lines. We utilize a hybrid work model and our teams are in-office Tuesdays Wednesdays and -person attendance is essential for this roles success and remote placement will not be offers relocation based on actual job level.

Time Zone Requirements
Primarily PT

While the position will primarily work local hours LendingClub is headquartered in Pacific Time and our ideal candidate will be flexible working across time zones when necessary.

Travel Requirements
As needed travel to LendingClub offices and/or other locations as needed.

Compensation
The target base salary range for this position is 00. The base salary of the role will be determined by job-related knowledge experience education skills and location. Base salary is just one part of LendingClubs Total Rewards package. You may also be eligible for long-term awards (equity) and an annual bonus (which is based on company performance employee performance and eligible earnings).

Were creating new financial services solutions for our members based on fairness simplicity and heart and we treat our employees the same way. We offer a competitive benefits package that includes medical dental and vision plans for employees and their families 401(k) match health and wellness programs flexible time off policies for salaried employees up to 16 weeks paid parental leave and more.

#LI-Hybrid
#LI-JH1