The Identity Strategist acts as a subject matter expert to design fraud and identity strategies for enterprise customers throughout the implementation lifecycle. They are responsible for orchestrating launch readiness, resolving technical or regulatory bottlenecks, and ensuring successful solution adoption.
Advise enterprise customers on fraud and identity risk, design tailored verification strategies and workflows, and guide implementation from onboarding through launch. Coordinate cross-functional teams, resolve technical and regulatory blockers, validate implementation milestones, and support go-live readiness and execution.
The Customer Success Manager will own a portfolio of enterprise and mid-market accounts, acting as a value consultant to ensure measurable risk reduction and identity strategy optimization. They are responsible for monitoring customer health, driving adoption, managing renewals, and identifying expansion opportunities through proactive engagement.
You will act as a technical expert to guide prospective clients through the implementation of Jumio's identity verification solutions. This involves leading technical presentations, managing customer onboarding, and collaborating with internal teams to improve product features.
Lead the design and scaling of production-ready computer vision systems for biometric face recognition and attributes. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Lead the design and scaling of production-grade computer vision systems for biometrics, including face recognition and attribute detection. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Lead the design and scaling of production-ready computer vision systems for biometric face recognition and attributes. Own the end-to-end ML pipeline from data ingestion and curation to low-latency deployment on AWS.
Build and integrate ROS/ROS2-based modules to support robotic navigation and data collection workflows. Develop and maintain end-to-end machine learning pipelines for training, evaluating, and monitoring model performance in production.
Design and develop end-to-end computer vision models and pipelines for biometrics use cases such as face detection and recognition. Optimize models for low-latency inference and mentor other ML engineers on technical best practices.
Lead the design and development of computer vision and biometric systems, including face recognition and quality assessment. Own the end-to-end machine learning pipeline from data ingestion and synthetic data generation to production deployment on AWS.
The role involves leading the design and development of computer vision systems for biometrics, including rigorous fairness analysis and benchmarking of models. Responsibilities also include owning end-to-end ML pipelines from data ingestion to deployment and optimizing models for low-latency inference.
