Senior Machine Learning Operations Engineer, Lending Club

• 6+ years of experience in Machine Learning Engineering, Data Engineering, or MLOps
• Bachelor's 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

Desirable

• We are seeking a Sr ML Platform Engineer for our Data Platform Team to help build and scale the ML platform and infrastructure that powers data-driven decision-making across the company
• This role focuses on enabling multiple business functions such as credit risk, fraud detection, marketing, and operations - to experiment, deploy, and monitor machine learning models efficiently and responsibly
• You’ll work closely with data scientists, data engineers, and product teams to create a unified, secure, and compliant ML ecosystem that accelerates innovation while ensuring model transparency, performance, and governance
• 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)
• 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