Engineering Manager, ML Efficiency, 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 polyglot coding expertise in C++ and Python, and familiarity with Google's core infrastructure (XManager, BrainServer, SavedModel, Pathways).

• 8 years of experience with data structures and algorithms.

• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.

• Knowledge of bridging high-velocity prototyping (TVP) with permanent distributed enterprise scale (Franchises) via structured handoff packages ("graceful exits").

• Track record leading SWAT, Pathfinding, or Forward Deployed Engineering (FDE) teams in fast-paced startup or ambiguous enterprise environments.

About the job:

Like Google's own ambitions, the work of a Software Engineer goes beyond just Search. Software Engineering Managers have not only the technical expertise to take on and provide technical leadership to major projects, but also manage a team of Engineers. You not only optimize your own code but make sure Engineers are able to optimize theirs. As a Software Engineering Manager you manage your project goals, contribute to product strategy and help develop your team. Teams work all across the company, in areas such as information retrieval, artificial intelligence, natural language processing, distributed computing, large-scale system design, networking, security, data compression, user interface design; the list goes on and is growing every day. Operating with scale and speed, our exceptional software engineers are just getting started -- and as a manager, you guide the way.

With technical and leadership expertise, you manage engineers across multiple teams and locations, a large product budget and oversee the deployment of large-scale projects across multiple sites internationally.

As a Engineering Manager on the ML Efficiency team, you will serve as a pivotal player-coach, driving both technical architecture and formal engineering management for a high-performing team of AI/ML systems engineers.

As an Engineering Manager, you will balance deep technical contributions with strategic pod leadership. You will lead Strike Sprints and embedded Forward Deployed Engineering (FDE) teams partnering with leadership across Google. You will take vague, high-stakes VP-level efficiency mandates, perform deep architectural surgery on enterprise pipelines, architect robust Thinnest Viable Proofs (TVPs), and cultivate an exceptional, high-velocity engineering culture.

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.

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 pathfinding and system design for the ML Efficiency Hub, driving complex 1–6 month Engineers and 2–4 week Strike Sprints.

• Design, prototype, and write production C++ and Python code alongside your team for model distillation, speculative decoding, dynamic batching, and distributed serving systems.

• Take ill-defined executive mandates ("The Hot Plate"), quickly de-risk technical feasibility within strict latency, FLOPs, and tokenomics thresholds, and deliver highly persuasive TVPs.

• Perform deep compute surgery on legacy P0 pipelines, evaluate complex architectural trade-offs, and establish concrete "Graceful Exit Packages" that set partner catching teams up for permanent autonomy.