HOPPR

• Work side-by-side with clinical, research, and industry partners to understand their imaging AI challenges. • Translate clinical workflows and regulatory requirements into technical specifications. • Serve as the technical face of HOPPR during deployments. • Build and customize pipelines to support AI model fine-tuning, validation, and deployment. • Write production-grade code to integrate APIs, data pipelines, and ML models into customer systems. • Prototype rapidly while ensuring scalability and compliance. • Lead on-site or remote deployments, ensuring customer success. • Troubleshoot integrations across hospital PACS, cloud infrastructure, and enterprise systems. • Monitor system performance and optimize for reliability, speed, and compliance. • Partner with HOPPR’s product, ML research, software engineering and compliance teams to extend platform capabilities. • Bring customer feedback into product roadmaps, helping shape features that serve the broader market. • Contribute to documentation, demos, and developer resources for customers and internal teams.

• Design and develop robust pipelines using advanced methods with large language models (LLMs) to extract features and label data from unstructured datasets. • Create and implement rigorous evaluation metrics to assess feature extraction processes, ensuring continuous improvement aligned with clinical and product goals. • Enhance and maintain scalable, reproducible data science infrastructure to support agile development and secure operations across partitioned client environments. • Design and implement MLOps practices to streamline, scale, and automate machine learning workflows. • Manipulate, analyze, and manage large-scale datasets using Python, SQL, and other tools. • Work closely with engineers, clinicians, and product teams to ensure data solutions are aligned with user needs and drive meaningful outcomes. • Thrive in a dynamic and rewarding environment that emphasizes excellence, autonomy, and impact.

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• Own a portfolio of enterprise accounts and leads. • Build and maintain a data-driven pipeline that supports market growth. • Lead sales cycles through discovery, qualification, planning, pricing, contracting, and signature. • Maintain rigorous pipeline hygiene, including stage definitions and forecast accuracy. • Establish multi-threaded relationships with key stakeholders. • Identify and address objections; align stakeholders to a clear buying process. • Close new business and expand within existing accounts.