Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- 5 years of experience with one or more of the following: speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience integrating generative AI tools or LLM interfaces into workflows.
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.
- Experience with low-level accelerator programming and compilation toolchains (e.g., XLA, Pallas, CUDA, Triton, or custom TPU kernels).
- Proven ability to lead rapid prototyping pods (SWAT/FDE) in highly ambiguous environments and influence VP/Director-level technical roadmaps.
- Demonstrated track record of optimizing large-scale production ML serving or training systems resulting in measurable, compute/cost savings.
About the jobWith your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
On the AI Rapid Response Team, you will serve as the primary technical anchor and chief efficiency strategist for Google's most demanding AI workloads. You will operate as an "ambiguity buster," taking nebulous VP-level mandates around compute constraints, latency spikes, and infrastructure scaling costs, and translating them into crisp, mathematically validated engineering solutions.
Trading long-term maintenance of legacy systems for continuous zero-to-one pathfinding velocity, you will lead Strike Sprints and embedded FDE engagements across Google. You will write high-performance production code, architect novel model efficiency pipelines, optimize inference serving engines, and establish graceful exit architectures that empower partner teams to run permanently lean.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Lead technical architecture and system design for complex 1-6-month Forward Deployed Engineer (FDE) embeds and 2-4-week Strike Sprints targeting high-leverage Machine Learning efficiency bottlenecks.
- Design, prototype, and write robust production C and Python code for model compression, speculative decoding engines, dynamic batching layers, and high-throughput serving pipelines.
- Diagnose subtle distributed latency and throughput bottlenecks across XManager, Pathways, and Tensor Processing Unit/Graphics Processing Unit clusters, implementing low-level kernel and memory optimizations (accelerated linear algebra (XLA), Pallas, Custom Ops).
- Deconstruct ill-defined executive mandates ("The Hot Plate") into rigorous efficiency scopes within strict latency, floating point operations (FLOPs), and tokenomics thresholds-delivering compelling thinnest viable proofs (TVPs).
- Design automated distillation, pruning, and quantization (FP8/INT4) pipelines that convert massive foundation models into compact, ultra-efficient student models without compromising evaluation benchmarks.
