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What if your Python automation caught a critical ASIC bug before the physical silicon was even manufactured? Our simulation platforms make that happen every release cycle! We are the System Ethernet QA Team in our R&D center, responsible for the testing and qualification of virtual networking silicon (SimX) and software-from L2 forwarding and L3 routing to overlays, QoS, and security. With SimX, developers and QA engineers get hardware-free, early access to the ASIC for better debugging.

We are looking for a Senior QA Software Engineer who combines network protocol expertise with strong Python automation skills. Does debugging a BGP EVPN route leak excite you as much as refactoring a test framework? Then we should talk!

What you'll be doing:

We don't just complete checklists; we collaborate closely with R&D to lead the quality of new features from design to deployment. In this role, we:

Review architectural designs and requirements for new features introduced to the product.

Drive the design, development, and execution of tests across functional, performance, and security scopes as part of SW GA and update releases.

Develop Python automation suites built on clean OOP principles.

Chase down defects and validate fixes until we fully resolve the root cause.

Leverage the SimX platform to validate code and debug the ASIC long before hardware arrives.

Lead a team of compiler engineers responsible for open source based toolchain and graph compiler. You will be a hands-on team leader - driving architecture decisions, guiding engineers, and delivering compiler capabilities across all hardware targets. Our team embraces an AI-agentic development flow, leveraging AI coding agents to accelerate engineering productivity, and we are looking for a leader who champions this approach.

Responsibilities

Lead development of open source based toolchain targeting custom DSP and NPU architectures. Drive design and delivery of graph compilation flows: transformations, operator mapping, and code generation. Champion AI-agentic development practices within the team - integrating AI coding agents into day-to-day engineering workflows. Mentor engineers and conduct code reviews; build a high-quality engineering culture. Collaborate with hardware architecture, algorithms, and software teams globally. Own team planning, prioritization, and on-time delivery of toolchain features. Contribute hands-on to design, implementation, and performance tuning in C++ and Python.

We are now seeking a highly motivated Software Verification Manager to join our Switching group, working on SONiC Network OS. In this role, you will manage SONiC Verification team that will develop automatic tests and lead Regression analysis for SONiC OS running over switches.

What youll be doing:

Lead and mentor SONiC Software verification team; set technical direction, define priorities, and grow team capabilities.

Design and implement robust, maintainable, and efficient automated test suite for SONiC OS which is an open-source NOS.

Lead regression analysis for SONiC NOS following regression automated tests.

Cross-functional work with SONiC Design teams, QA, architecture, FW/SDK/SAI/platform teams.

We are building the software foundation for the next generation of AI infrastructure. As AI workloads continue to evolve, Kubernetes and the cloud-native ecosystem must evolve with them.

Our teams mission is to make Kubernetes the best platform for AI workloads. We work upstream with the Kubernetes and CNCF communities to design new APIs, build production-grade implementations, and shape the future of cloud-native infrastructure for AI.

As a Senior Software Engineer, you'll tackle the most challenging problems at the intersection of Kubernetes, distributed systems, and AI infrastructure.


What you'll be doing:

Design, implement, and upstream new capabilities for Kubernetes and CNCF projects.

Collaborate with engineering teams across us to identify AI infrastructure challenges and solve them through upstream innovation.

Work closely with Kubernetes SIGs, Working Groups, and the broader open-source community to design and implement new capabilities.

Participate in architecture discussions, API design, technical proposals, and code reviews.

Build reliable, scalable infrastructure software for next-generation AI workloads.

Write clear technical documentation and design proposals.

In this role, as Embedded Software Team Leader, you will lead a team owning a key part of NPU AI software stack, a complex, multi-layered system spanning the Host (SDK, runtime, APIs, and UMD/KMD drivers) and the NPU IP (embedded firmware). This stack is at the heart of the AI software offering, with deep impact on multiple hardware and software teams ability to execute state of the art LLM, Generative AI and Neural Networks with extraordinary performance. Reporting to the AI Software SDK Senior Team Leader, you will lead a team of creative and result oriented enthusiasts that invent, design, and implement software that enables smooth connectivity between the NPU and Host CPU.



Responsibilities:

Lead and manage the Embedded Software engineering team, ensuring high performance and productivity.

Own the design, development, and implementation of your component within NPU AI software stack, aligned with the AI Software SDK group's technical strategy, architecture, and roadmap and its automated CI and release process.

Collaborate with cross-functional teams to integrate solutions. Provide technical guidance and mentorship to team members.

Ensure project timelines and deliverables are met.

Conduct performance reviews and support professional development of team members.

Collaborate globally with architects, HW teams, NPU teams, and other developer teams

In this role, you will be a key contributor to the design and implementation of AI Graph Compiler software stack for Neural Processing Units (NPUs). You will take part in defining software architecture, implementing performance-critical components, and enabling efficient execution of advanced neural networks under tight power, memory, and latency constraints.

You will work closely with hardware and system architects, software and hardware engineers, influencing both software and hardware decisions. You will design and implement major parts of NPU embedded solutions, actively promoting AI capabilities to the customers.

What will you do:

Own and design key components of the AI Graph Compiler software stack for NPU-based systems.

Optimize inference performance (latency, throughput, memory footprint, power) for edge deployments.

Collaborate on HW-SW co-design, influencing NPU architecture.

Support IP evaluations and silicon bring-up, root-cause complex HW/SW issues, and influence development methodologies.

Mentor junior engineers and contribute to technical best practices.