Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 8 years of experience programming in C++ or Python.
- 5 years of experience testing and launching software products.
- 5 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
- 3 years of experience with software design and architecture.
Preferred qualifications:
- Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 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 in performance optimization, performance tuning, operating systems, virtual machines, etc.
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.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
The Platforms AI-augmented Compute Efficiency (ACE) team is dedicated to addressing the affordability gap through focused efforts on scaling, adapting, and simplifying the software/hardware interfaces amidst the swift evolution of customer demands. Our mission is to collaboratively enhance the efficiency, performance, reliability, and corruption resilience of new compute platforms such as CPUs, TPUs, and GPUs, ensuring these innovations are effectively aligned with the urgent needs of our cloud and machine learning fleets. We are committed to maintaining high standards of innovation and service, continuously pushing the boundaries to meet and exceed industry benchmarks and customer expectations.
Our Platforms Infrastructure Engineering team designs and builds the
hardware and software technologies that power all of Google's services. Our computational challenges are complex and unique, enabled by cutting-edge custom hardware designed and made in-house. As a hardware engineer, you will design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You will see those systems from concept all the way through to high-volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers, affecting millions of Google users.
The US base salary range for this full-time position is $197,000-$291,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
- Accelerate the ‘shift-left’ adoption of new platforms by product areas, ensuring optimal product performance and reliability from the moment of general availability by encouraging early adoption of customer workloads.
- Evaluate performance of customer workloads on VMs and optimize the end-to-end stack including VMM and Guest OS.
- Define robust methodologies with scalable tooling and fleet metrics to track performance, reliability, and efficiency.
- Promote the adoption of model/data efficiency techniques such as leveraging new hardware capabilities to enhance ML model architecture for more efficient task resolution and employing techniques to reduce downtime and improve throughput, thereby enhancing overall efficiency.
- Identify and drive the need for forward-looking ML training and benchmarks that represent Google production and the ML community.
