Senior Machine Learning Engineer

Credit Karma•Charlotte, NC

About The Position

Credit Karma is looking for a results-oriented and skilled Machine Learning Engineer to join our team, focusing on building and deploying the infrastructure, services, and SDKs enablingCredit Karma’s Data Science teams to prototype, deploy, score, and monitor predictive models at scale. The ideal candidate will have expertise in MLOps, big data technologies, software development, data engineering, deep learning ML frameworks, and is driven to stay current with the fast moving ML & AI landscape and integrate innovations into our platforms.

Requirements

MS in Computer Science, Mathematics, Statistics, Machine Learning, or a related quantitative discipline
7+ years of industry experience in Machine Learning, Data Science and related areas, ideally in hyper-growth consumer Internet scenarios
Deep understanding and ability to architect and develop next-generation ML systems, staying ahead of industry trends and integrating latest advancements (e.g. GenAI)
Strong background in programming languages (e.g., Python, Java, SQL)
Experience with deep learning frameworks (e.g., Tensorflow, PyTorch)

Nice To Haves

MLOps & Infrastructure: Experience with managing a large-scale platform that services many hundreds of auto-refreshing machine learning models deploying into production with no human-in-the-loop.
Communication: Exceptional ability to communicate technical concepts to non-technical stakeholders and drive organizational alignment.
Distributed Processing: Experience with high-volume data processing and frameworks like Spark, Dataflow, Dask, Ray.
Deep Learning: Extensive Experience with deep learning frameworks such as Tensorflow or PyTorch.
Google Cloud: Experience with managing platforms backed by Google Cloud ML and AI services.

Responsibilities

Training Platform : Design, build, and maintain our Next Generation federated ML Platform - built on Vertex AI and Kubernetes. Contribute to our python SDK, which enables Data Scientists to efficiently develop, define, and deploy no-human-in-the-loop auto-refreshing deep learning and tree-based ML Models.
ML Features Platform: Design, build, and maintain our feature engineering and feature stores services supporting batch and streaming features - built on Vertex featurestore, Chronon, Databricks Tecton.
Training Data: Design and build out capabilities supporting training data pipelines and centralized modeling data stores. Integrate labelbox, snorkel, and Intuit’s GenAI and human-backed AI labeling platform.
Technical Support & Collaboration: Provide technical support for owned products, including performing on-call duties, resolving production site issues, and improving the performance and scalability of services. Collaborate with cross-functional stakeholders to identify high-impact opportunities, translate business and analytical requirements, develop project plans, and report business value.
Production Deployment and Monitoring: Platform-level monitoring for features, training data, training, and offline batch scoring. Provide utilities and capabilities to enable Data Scientists to do pipeline-level monitoring for training and scoring.
Innovation & Mentorship: Stay current on innovation trends and propose solutions that integrate those back into our platform. Support & mentor other members of our team on current trends, best practices, and their projects.