About the job

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.

In this role, you will deliver credible machine learning (ML) and artificial intelligence (AI) performance and cost projections, you will influence the machine learning hardware and software (HW/SW) roadmap through co-design and key insights, and foster full stack innovation.

The ML, Systems, & Cloud AI (MSCA) organization at Google designs, implements, and manages the hardware, software, machine learning, and systems infrastructure for all Google services (Search, YouTube, etc.) and Google Cloud. Our end users are Googlers, Cloud customers and the billions of people who use Google services around the world.

We prioritize security, efficiency, and reliability across everything we do - from developing our latest TPUs to running a global network, while driving towards shaping the future of hyperscale computing. Our global impact spans software and hardware, including Google Cloud’s Vertex AI, the leading AI platform for bringing Gemini models to enterprise customers.

Responsibilities

• Define, implement, and validate performance and cost metrics for current and future workloads; analyze systems from chip to network to identify bottlenecks and optimize performance.

• Co-design hardware and system features, including compute, networking, and memory, for evolving workloads while identifying future hardware and software opportunities to improve ML performance.

• Build, maintain, and validate high-fidelity Hardware and Software (HW/SW) simulators in C++ and Python to enable rapid evaluation of solutions, alongside developing methods to measure, visualize, and analyze ML performance.

• Develop and maintain comprehensive benchmarks and, when appropriate, propose changes to ML models and systems to enhance overall infrastructure efficiency and end-to-end results.

• Collaborate cross-functionally with computer architects, hardware designers, and ML model owners to communicate findings and turn analysis results into actionable hardware and software roadmap improvements.

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.

• 8 years of experience with software development in C++ and Python.

• 5 years of experience with ML infrastructure, including performance analysis and optimisation of distributed systems.

• 5 years of experience modeling or evaluating performance for large-scale machine learning workloads (e.g., Large Language Models).

• Experience with hardware-software co-design, ML accelerators, and low-level software stacks such as compilers.

Preferred qualifications

• Master’s degree or PhD in Computer Science or a related technical field.

• Experience in the self-driving or automotive industry building large-scale distributed infrastructure and networking systems.

• Experience in building high-fidelity HW/SW simulators (C++/Python) and large-scale performance tooling for ML accelerators.

• Understanding of large-scale AI workloads (LLMs), agentic systems, and performance/cost analysis for hyperscale systems.

• Knowledge of compiler flows, hardware-software co-design, and translating frameworks like Py Torch or JAX to hardware instructions.

• Ability to collaborate cross-functionally to translate performance insights into actionable hardware roadmap improvements.