• Build platform capability hands-on: design and ship core platform services end to end, from architecture through production and ongoing operation, as self-service capabilities that operating-company engineers adopt with minimal friction. • Steward the platform's architecture and standards, shaping the reference architecture, golden paths, and paved roads, and turning engineering standards into executable guidance through the Architecture Council. • Advance AI-first software delivery by building and evolving the templates, pipelines, and golden paths that raise developer throughput, and help extend the platform into shared services that support new software offerings. • Own reliability, security, and cost for the services you build: define SLIs and SLOs, instrument observability, run incident response, and reduce toil through automation, with security and cost designed in from the start. • Set the technical bar and mentor: raise the level of platform and operating-company engineers through hands-on engineering, code review, and mentorship, and help build a connected engineering community across the portfolio.

• Drive engineering quality and reliability of Dyno’s data platform, ensuring access, security, and data lifecycle integrity. • Translate scientific and clinical questions into production-ready systems, including APIs, pipelines, and user-facing tools. • Support team members through code and architectural review to systemically increase engineering quality. • Move quickly from prototype to production, balancing speed with reliability. • Collaborate cross-functionally, leveraging Dyno’s high-trust, high-impact culture to drive results.

• Define and drive the technical roadmap for platform infrastructure, reliability, and scalability initiatives. • Design and review complex system architectures that support company growth and evolving business needs. • Establish engineering standards, best practices, and technical patterns across multiple teams. • Improve system observability, incident response processes, and operational excellence across the platform. • Partner with Engineering, Product, and Design leaders to align technical investments with business priorities. • Mentor engineers and provide technical guidance that elevates engineering capabilities across the organization. • Build and improve backend services, infrastructure tooling, and shared platform capabilities. • Identify technical debt, scalability risks, and operational vulnerabilities before they become business constraints.

• Design, build, and evolve core ML platform infrastructure, including feature stores, real-time model scoring services, and systems supporting the full model development, deployment, and monitoring lifecycle. • Drive technical decision-making for complex initiatives, choosing solutions that scale, are testable, and reduce long-term maintenance burden. • Lead and influence system design discussions, clearly articulating trade-offs and aligning solutions with product and business goals. • Set a high bar for code quality and system reliability through exemplary contributions and thoughtful, constructive code reviews. • Identify, communicate, and mitigate technical risks across platform components before they impact members. • Partner closely with data scientists, engineers, and product stakeholders to translate modeling and business needs into durable platform capabilities. • Provide clear, reliable estimates for complex projects, including assumptions, risks, and dependencies. • Improve team processes, tooling, and standards to increase engineering quality and delivery velocity. • Mentor and support other engineers through design feedback, code reviews, and onboarding. • Participate in hiring and interviews, helping raise the technical bar through well-calibrated feedback.