• Use the latest cutting edge technologies such as LLMS, multimodal models to handle complex problems. • Work with large datasets, perform data preprocessing, and engineer relevant features to enhance model performance. • Build frameworks that allow us to iterate and evaluate model versions (ranking, accuracy, latency). • Deploy Models at Scale: Collaborate with software engineers to deploy machine learning models into production, ensuring seamless integration with existing systems. • Monitoring and Maintenance: Implement monitoring solutions to track model performance in real-time and perform regular maintenance and updates as needed. • Collaboration: Work closely with cross-functional teams, including data scientists, software developers, and business analysts, to understand requirements and deliver impactful solutions. • Research and Innovation: Stay abreast of the latest advancements in machine learning and contribute to the research and development of innovative solutions.

• Design, implement, and train discrete and continuous diffusion models for predicting biomolecular structure tokens • Develop and iterate on structure tokenizers, including vector-quantized representations of 3D molecular and protein structure • Build and maintain data processing pipelines for large-scale biomolecular structure datasets • Train models on multi-GPU clusters, managing large-scale training runs • Develop rigorous benchmarking and evaluation workflows; validate against external benchmarks while prioritizing internal discovery-relevant metrics • Collaborate with ML scientists, computational chemists, and drug discovery teams to integrate models into discovery workflows • Communicate results to internal teams, external partners, and at scientific conferences • Mentor interns and junior team members through code reviews, technical guidance, and best practices (Senior level)

• 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, develop, and deploy Cresta’s AI Agent solutions and proprietary models. • Focus on practical AI challenges such as improving reasoning, planning capabilities, and evaluation in real-world scenarios. • Collaborate with cross-functional teams including front-end and back-end software engineers to integrate AI Agents into Cresta’s customer solutions. • Lead initiatives to scale AI systems for production environments, ensuring performance and reliability across use cases. • Contribute to solving cutting-edge problems in AI and help define the future roadmap for Cresta’s AI Agents. • Innovate and research ways to improve security, cost-efficiency, and reliability of AI systems.