Staff AI/ML Engineer, AI Rapid Response Team

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 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.

With 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.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

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.