Machine Learning Engineer

This role is for an experienced Machine Learning Engineer to design, train, deploy, and optimize models on large-scale temporal, geospatial, relational, and unstructured datasets. The engineer will work across computer vision, NLP, geospatial analytics, and MLOps, and will own the full ML lifecycle from data pipelines to monitoring. The position is fully onsite in Menlo Park and emphasizes knowledge graphs, model governance, and collaboration with distributed teams.

• Design, train, evaluate, deploy, and optimize machine learning models across production use cases
• Build predictive solutions for anomaly detection, forecasting, entity resolution, relationship prediction, and risk assessment
• Develop and maintain ML pipelines from data preparation through feature engineering, training, evaluation, and monitoring
• Build scalable solutions leveraging graph-based and knowledge-graph-driven data architectures
• Implement monitoring, observability, alerting, retraining, rollback, and model governance processes
• Collaborate with data engineering and distributed engineering teams to deliver reliable ML capabilities

• Experience owning the full machine learning lifecycle in production environments
• Broad expertise in computer vision, NLP, geospatial analytics, and/or MLOps
• Strong understanding of model architecture selection and algorithm design aligned to business objectives