• 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.

• Strategic product leadership: Responsible for the product vision, strategy, and roadmap for platform engineering products, aligned with company priorities and engineering needs. • Platform-as-product: Treat internal capabilities like real products, using discovery, user research, feedback loops, and adoption data to solve real problems for creators. • Stakeholder management: Coordinate engineers, product managers, engineering leads, security, SRE, and executive leadership to align on a clear platform direction. • Impact ownership: Define and track success metrics such as platform adoption, change lead time, MTTD/MTTR. This includes translating complex technical platform initiatives and metrics into clear business interests and ROI for non-technical (C-level) stakeholders. • Internal product evangelism: Communicate complex technical products clearly and persuasively so teams understand, trust, and adopt them. • Agentic engineering enablement: Help design the tools, context layers, and guardrails that enable people and AI agents to safely build and release software. • Prioritization and trade-offs: Balance speed, reliability, security, simplicity, and long-term maintainability in a fast-paced scaleup environment.