Freelance High Performance Computing Engineer Jobs in Milpitas, CA

• Bachelor's degree or equivalent practical experience.
• 8 years of experience programming in C++.
• 5 years of experience with design and architecture, and testing and launching software products.
• Experience with C, High Performance Computing, Remote Direct Memory Access, Storage Systems, System Design, and Kernel Drivers.

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with data structures and algorithms.
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience with Storage Systems or Machine Learning Infrastructure.

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

The High Performance Networking (HPN) team’s mission is to deliver scalable, state-of-the‑out high-performance networking solutions to support a various range of high performance workloads for Google and Google Cloud .

TheHPN team is at the core of Google's AI infrastructure. We design, deliver, and sustain Google's end-to-end RDMA stack, comprising:

• Core IPU components and Linux kernel/Guest RDMA drivers.
• Industry-leading acceleration technologies such as TPUDirect, TPUDirect Storage (TDS), and GPUDirect Storage (GDS).

As a Senior Staff Software Engineer, you will lead the evolution of this stack. You will drive the integration of the high-performance networking layers with industry-leading Parallel File Systems (PFS) and storage servers. Your technical leadership will directly shape the efficiency, scalability, and performance of Google's hyperscaler platform for next-generation AI/ML training and inference workloads.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

• Technical Leadership and Mentorship: Lead and coach a distributed engineering team, fostering innovation while aligning cross-functional groups on goals, priorities, and deliverables.
• Large-Scale and LLM Architecture: Design and deploy scalable software and advanced serving architectures, optimizing the LLM serving stack for efficiency and reliability.
• Performance and Network Engineering: Architect performant network transport across various NICs and platforms, tuning the communication stack to accelerate HPC, RDMA, and ML workloads.
• Strategic Roadmap and Trends: Partner with cross-organizational leaders to co-develop roadmaps bridging RDMA, storage, and AI/ML, staying ahead of training and inference advancements.
• Collaborative System Integration: Collaborate with hardware, software, and systems engineers to bridge communication gaps and ensure seamless hardware-software integration.