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X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 5 years of experience with software development in C and Python.
- Experience with functional/architectural simulators, emulator development, or compiler backends.
- Experience with computer architecture, instruction set architectures (ISAs), and hardware-software interaction.
Preferred qualifications: - Master's degree or PhD in Computer Science or related technical field.
- 5 years of experience with data structures/algorithms.
- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.
About the jobAs a Senior Software Engineer, you will design the next generation of AI accelerator instruction set architectures (ISAs) to power frontier ML models at scale. You will architect novel instruction primitives and addressing modes purpose-built for model dynamics-including MLA, speculative decoding, and dynamic sparsity. By pioneering AI-driven exploration pipelines, you will leverage LLMs and autonomous agents to explore vast architectural design spaces, prototype simulator features, and validate hardware-software trade-offs across compiler backends and RTL pipelines.
Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we're working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Design custom instruction sets, addressing modes, and hardware features targeting emerging frontier model operations (e.g., MLA, speculative verification, dynamic sparsity, multi-precision numerical formats).
- Deploy agentic orchestration and automated exploration pipelines to synthesize ISA configurations, generate test kernels, and discover architectural bottlenecks autonomously.
- Collaborate across ML research, XLA compiler, and RTL teams to ensure new instructions are seamlessly targetable, efficient to lower, and verify cleanly across simulation environments.
- Implement instruction-level simulators, golden reference models, and trace analyzers to measure performance, silicon area, and compiler efficiency.
- Provide rigorous, data-driven architecture proposals to guide multi-generational silicon roadmaps.
