• You build and ship end-to-end features across backend (Ruby on Rails) and frontend (React) • You design and implement AI-driven product capabilities (e.g. agentic workflows, AI-assisted features) • You work close to the customer, investigating and resolving user issues first hand • You take ownership of features from idea to production and iteration • You improve system architecture, scalability, and maintainability • You collaborate closely with Product, Engineering, and GTM to solve real logistics problems • You contribute to technical decisions and roadmap planning • You support and unblock other engineers

• Design and evolve core platform services and internal APIs consumed by multiple teams • Drive architectural initiatives such as gRPC migration, eventing/pub-sub foundations, and platform refactors • Own and improve control-plane and cluster-api components • Build platform abstractions that reduce operational and cognitive load for other teams • Write production-grade Go code for platform services and internal tooling • Collaborate closely with Regions & Clusters, Resilience, and Features teams to enable delivery through platform primitives • Improve platform scalability, reliability, and developer experience through thoughtful design • Support production systems and incident response when needed, with a focus on long-term fixes

• Systems Requirements Definition: Develop and document detailed product-level and component-level requirements, ensuring alignment with clinical needs, user feedback, and safety critical system performance. • Risk Management: Conduct system-level risk analysis, identifying hazards and harms, and recommending control measures that meet medical device regulatory standards (ISO 14971). • Traceability: Ensure full traceability from requirements through design, implementation, testing, and verification to comply with medical device standards (ISO 13485, IEC 62304), ensuring that every system requirement is tested and verified. • Testing and Validation: Collaborate with the testing team to develop system-level test plans, protocols, and verification/validation activities to ensure the product meets functional and safety requirements. • System Integration: Lead the integration of multi-disciplinary components (software, hardware, mechanical) to ensure cohesive system functionality and performance in real-time surgical environments. • Regulatory Compliance: Ensure compliance with all relevant medical device regulations, including documentation for regulatory submissions. Provide technical support for regulatory audits and submissions. • Cross-functional Collaboration: Work closely with software, hardware, and mechanical teams to ensure design decisions are optimized for the overall system. Collaborate with product management, clinical engineering, and surgeons to ensure the system meets user requirements and delivers value. • Verification of Design: Lead the effort to verify that system designs meet system requirements through rigorous testing and simulation. Lead system design reviews to ensure requirements are met and safety standards are maintained. Drive system level risk identification and define robust risk control measures, including oversight of risk control implementation and verification of their effectiveness. • Validation of Design: Lead design reviews to ensure design outputs conform to approved design inputs and applicable regulatory requirements. Own design validation activities, including oversight and support for the preparation and maintenance of usability engineering deliverables in accordance with IEC 62366. • Change Management: Manage design changes and their impacts on system-level requirements, ensuring that changes are properly analyzed for risk and maintain compliance with regulatory standards. • Documentation: Lead the creation of system documentation, including requirements specifications, design documents, risk management files, and traceability matrices to ensure complete, clear, and compliant records throughout the development lifecycle.

• Own the parser framework: identify patterns worth abstracting, design the primitives parser authors build on, improve performance on hot paths, and raise the reliability and testability bar across the team's output. • Make the design calls that require judgment—schema mapping, normalization trade-offs, how to handle ambiguous or malformed data, when to generalize versus when to special-case, when to evolve the framework versus work around it. • Drive high-impact parser integrations end-to-end where framework-level thinking is needed, setting the pattern that others follow. • Partner with detection, data, and integration teams to make sure parsed data serves downstream use cases, not just passes validation. • Mentor junior engineers through design discussions and code reviews, helping them grow into independent owners. • Accelerate your own parser development using LLM-based coding assistants, AI-driven test generation, and automated code review. • Use LLMs to analyze unfamiliar log samples, propose initial parsing rules, and bootstrap new integrations faster—while applying human judgment to catch what AI gets wrong. • Automate repetitive parser work—regression testing, schema diffing, sample ingestion—so the team spends more time on hard problems. • Help the team integrate AI tools into their daily parser workflows and measure the efficiency gains.