Sr ML Ops Engineer

LendingClub • San Francisco, CA

21h • Hybrid

About The Position

Our mission at LendingClub is to empower those who strive to achieve better financial health. The data Platform Team plays a crucial role in achieving our mission. 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.

Requirements

• 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.

Nice To Haves

• 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

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)
• 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

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees