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Senior Software Engineer, Generative AI, Google Research

Google
Sunnyvale, USA
Senior · 5+ years experience
Onsite
USD 174000-252000 / year
Discovered 2 weeks ago
jaxpythonpytorch

Full Job Posting

Responsibilities

  • Design, build, and maintain scalable performance simulation frameworks to accurately evaluate training and serving metrics for next-generation TPU configurations.
  • Model how future model architectures (e.g., Mixture-of-Experts, multi-head latent attention, hybrid sequence parallelism, sparse attention, and KV-cache compression) interact with architectural variables (e.g., SRAM capacity/bandwidth, HBM contention, ICI network topologies, and live failover mechanisms).
  • Develop and integrate AI-agentic platforms and toolchains to automate simulator sweep setup, parameter sensitivity analyses, and data extraction over gigabytes of simulation sweeps—reducing analysis cycles from weeks to hours.
  • Build analytical and optimization engines (e.g., Mixed-Integer Programming/MIP, simulated annealing, roofline estimation) to identify Pareto-optimal execution strategies (sharding, tensor placement, weight pinning, prefetching).
  • Formulate clear, data-grounded performance projections and comparative studies (e.g., Iso-execution analysis, trade-off studies between compute density versus memory bandwidth) to inform multi-year TPU platform roadmaps and hardware contracts.

Minimum qualifications:

  • Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, a related technical field, or equivalent practical experience.
  • 5 years of software engineering (e.g., C++ and Python).
  • Experience with computer architecture concepts (memory hierarchies, roofline models, bandwidth versus compute bottlenecks, network topologies).
  • Experience with software design for simulation, performance modeling, or scientific computing.

Preferred qualifications:

  • Master's degree or PhD in Computer Science, or a related technical field.
  • Experience designing LLM-based agentic orchestration loops (e.g., autonomous tool calling, multi-turn reasoning, self-correcting execution) or applying AI to automated software engineering, hardware/compiler optimization, or simulation workflows.
  • Experience with performance profiling tools, analytical roofline modeling, or architectural simulators.
  • Knowledge of modern transformer architectures (Dense and MoE, long-context attention, speculative decoding) and distributed execution paradigms (SPMD, pipeline, tensor, and expert parallelism) across modern ML frameworks (e.g., JAX, PyTorch/XLA, or Pallas).
  • Ability to bridge high-level model specifications with low-level hardware constraints (interconnect latency, SRAM caching, DMA scheduling).

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