• Design and develop AI-powered developer tools using Claude-based technologies. • Build intelligent agents and automated workflows that improve SDLC efficiency. • Develop and orchestrate multi-agent systems using modern agentic frameworks. • Rapidly prototype and iterate proof-of-concept solutions for AI-driven automation. • Integrate AI-powered tools with existing engineering platforms and services. • Collaborate closely with senior technical leadership on innovation initiatives. • Translate strategic ideas into working prototypes and production-ready solutions. • Ensure solutions are scalable, maintainable, and aligned with engineering best practices. • Participate in architecture discussions and contribute to the evolution of AI-enabled engineering workflows.

• Lead design and development of Linux (alike) kernel components, including networking/switch drivers, subsystems, and performance-critical paths • Provide architectural guidance on kernel interfaces, memory management, interrupt handling, DMA, and concurrency • Review and guide complex kernel patches with a focus on correctness, performance, maintainability, and upstream acceptance • Drive root-cause analysis and resolution of complex kernel-level issues across development, CI, and customer environments • Manage, mentor, and grow a team of Linux (alike) kernel engineers, including goal setting, performance feedback, and career development • Build a high-trust, fast-paced engineering culture emphasizing code quality, ownership, and collaboration in a startup environment • Partner with recruiting to hire software engineers and grow team capabilities with AI tools • Champion upstream development and active participation with the Linux kernel, FreeBSD, and DPDK community (LKML, maintainers, reviews) • Ensure patches meet community standards and are submitted, reviewed, and maintained upstream where appropriate • Manage the team to participate in technical discussions with external open-source stakeholders • Work closely with hardware, firmware, networking, systems, QA, and product teams to align kernel work with platform roadmaps • Translate product requirements into executable technical plans and milestones • Support customer escalations by coordinating deep technical investigations and delivering durable fixes • Own planning, prioritization, and execution of kernel deliverables across multiple releases • Establish and improve development processes including CI, testing, code review, and release readiness • Track progress, risks, and dependencies, communicating clearly with stakeholders

• Design and implement performance-critical features for CCL enablement on Cornelis Networks’ fabrics • Optimize distributed training performance across multi-node, multi-GPU configurations • Improve GPU communication paths including GPU-direct transfers, IPC, and CPU/GPU synchronization • Profile distributed AI workloads and identify bottlenecks across the software and hardware stack • Tune AI frameworks such as PyTorch Distributed, TensorFlow/XLA, JAX, DeepSpeed, and Megatron-LM • Develop benchmarks and microbenchmarks aligned with real model performance • Contribute upstream to AI communication and distributed training projects • Participate in design reviews, code reviews, CI, and long-term maintenance • Prototype and validate Ultra Ethernet capabilities for AI collective communication • Provide technical input for deployment considerations and performance validation • Collaborate with kernel/driver, switch, performance, and systems teams • Support advanced escalations by analyzing traces and providing robust fixes

• Help implement scalable and efficient data architectures, meeting data needs from data scientists, engineers and business partners • Actively participate in the team’s planning process, discussing team’s priorities and proposing project ideas as you grow your knowledge and ownership in data-infra systems • Identify and implement improvements to system’s cost efficiency, reducing data storage and processing costs • Participate in the team’s oncall rotation, helping solve timely reliability incidents