LendingClub is seeking to empower those who strive to achieve better financial health by building and scaling the ML platform and infrastructure that powers data-driven decision-making across the company.
Requirements
- 6+ years of experience in Machine Learning Engineering, Data Engineering, or MLOps
- 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
Responsibilities
- 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 LendingClub’s 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)
Other
- Bachelor's degree or higher in a related field; or equivalent work experience
- 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.
- In-person attendance is essential for this role’s success, and remote placement will not be considered.
- As needed travel to LendingClub offices and/or other locations, as needed.
