• 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.

• Lead the design and development of Cresta’s next-generation AI Agents and Agentic Assist systems, defining system architecture and core modeling approaches. • Architect intelligent, multi-step agent workflows that combine real-time guidance, knowledge retrieval, reasoning, summarization, and automated actions into cohesive production systems. • Design, deploy, and optimize LLM-powered systems, including Retrieval-Augmented Generation (RAG) pipelines, multi-agent orchestration, and domain-adapted models. • Improve reasoning, planning, and tool-use capabilities in real-world AI applications. • Develop evaluation strategies for complex, non-deterministic systems, including offline benchmarking, online experimentation, and LLM-as-a-judge methodologies. • Diagnose and mitigate real-world failure modes such as hallucinations, retrieval errors, tool misuse, prompt brittleness, and multi-step reasoning breakdowns. • Define and measure quality metrics (e.g., accuracy, faithfulness, task completion, latency, cost, robustness) to improve system reliability and performance. • Optimize AI systems for scalability, latency, security, and cost efficiency in production environments. • Collaborate cross-functionally with product, frontend, and backend teams to integrate AI capabilities seamlessly into Cresta’s platform. • Mentor engineers, contribute to technical strategy, and help shape the roadmap for Cresta’s AI systems.