• Developing both the front-end and back-end systems that allow users to seamlessly interact with our models • Building robust backend services and APIs for model inference, including efficient batching systems and optimization for large datasets • Implementing state management and session persistence mechanisms to maintain model context and deliver consistent user experiences • Designing and developing responsive, user-friendly frontends for model interaction, monitoring, and configuration using modern frameworks (React, Vue, or Angular) • Designing event-driven architectures with pub/sub messaging patterns to enable asynchronous processing and system scalability • Developing database connectivity layers and integration points with third-party data sources (e.g., Snowflake, Databricks) • Creating comprehensive error handling, retry mechanisms, and monitoring solutions to ensure system reliability • Collaborating with MLOps and DevOps teams to optimize the overall architecture and deployment pipeline

• Own the translation of legal complexity into technical architecture • Design and implement sophisticated agentic workflows • Work across the stack: from backend Python services to React frontend • Collaborate directly with founders and the Product Manager

• Lead design and implementation enabling and optimizing HPC middleware (MPI and SHMEM) and AI middleware CCL stacks (e.g., NCCL/RCCL and related collective communication libraries) • Deliver performance-critical communication paths including low-latency small and medium message transfers, bulk SDMA data movement, GPU-Direct and IPC communication, and collective acceleration • Design and tune collective communication algorithms (latency-optimized and bandwidth-optimized), including GPU-aware collectives • Integrate middleware with underlying transports and provider layers such as libfabric/OFI, UCX, and verbs-style interfaces to achieve performance, portability, and maintainability • Implement and optimize memory registration strategies, progress and execution models, completion semantics, multi-rail communication behavior, and GPU memory handling • Drive upstream contributions across MPI/SHMEM projects, CCL ecosystems, and related components with a focus on upstreamable design and long-term maintainability • Represent Cornelis Networks in open-source communities through technical reviews, design discussions, and sustained technical leadership • Implement and prototype Ultra Ethernet capabilities supporting MPI/SHMEM and AI collective communication use cases • Collaborate with ecosystem partners to validate deployment models and performance scaling on customer-relevant configurations • Work closely with kernel, driver, and switch teams to deliver end-to-end performance aligned with the Cornelis product roadmap • Participate in architecture reviews, performance tuning, scaling validation, and multi-layer root-cause investigations • Analyze performance traces and triage advanced customer issues, translating findings into robust fixes and upstream improvements • Publish internal and external best practices, including tuning guidance, reference configurations, and debugging methodologies • Mentor senior engineers and promote best practices for design, testing, documentation, and code quality • Help define the long-term middleware technical roadmap aligned with product evolution and customer needs

• Work closely with clinical informatics and IT teams to define Epic integration requirements • Lead Epic integration design, development, and testing, ensuring quality and scalability • Coordinate with external partners (e.g. Epic consulting teams) to drive delivery • You’ll act as the SME on Epic integrations within our engineering team. • Build features across backend and frontend systems • Implement new services and API endpoints supporting clinical and operational use cases • Ship greenfield work, contributing to a modern tech stack that prioritizes speed and developer productivity • Partner with product, design, and other engineering teams to prioritize and deliver high-impact work • Ensure solutions meet security, privacy, and performance standards • Help define best practices and improve engineering processes