Senior Staff Machine Learning Engineer

Tapestry•Mountain View, CA

3d•Hybrid

About The Position

About Tapestry Tapestry is a team within Alphabet working to build the AI-powered electric grid. We are tackling one of the world’s most important infrastructure challenges: helping the energy system become more visible, understandable, reliable, affordable, abundant, and clean. Originally born at X, Alphabet’s moonshot factory, Tapestry brings together experts in energy, AI, software, engineering, and product to build tools that help the electricity ecosystem plan smarter, move faster, and operate more efficiently. This is a global effort. Tapestry supports partners across the U.S., U.K., Chile, New Zealand, Australia, and Brazil as they work toward a cleaner, more resilient energy future. Joining Tapestry means doing high-impact work with a multidisciplinary team tackling a problem that matters at global scale. Learn more about our team and our mission here . About the role: You will serve as a foundational architect of Tapestry’s multi-year machine learning strategy, bridging cutting-edge AI research, the physics of continental-scale power grids, and the development of production ML/AI systems. You will architect machine learning systems that advance grid planning, simulation, and asset intelligence at continental scale.

Requirements

A Master’s degree or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related quantitative field, or equivalent practical experience.
10+ years of professional experience building, training, and deploying large-scale machine learning systems in production, with deep proficiency in modern frameworks such as PyTorch, JAX, or TensorFlow.
4+ years of professional experience working with grid modeling, simulation, state estimation, or power-system optimization, including familiarity with physical grid constraints, utility data structures, or spatiotemporal modeling for the grid.
A demonstrated track record of architecting systems capable of handling massive datasets or highly compute-intensive, parallel workloads.
Experience collaborating across technical disciplines and functions, aligning stakeholders around complex architectural decisions, and mentoring senior technical leaders.
The ability to think from first principles and apply structured technical judgment to complex, ambiguous problems spanning machine learning, physical systems, and production infrastructure.
Strong written and verbal communication skills, with the ability to communicate complex technical concepts clearly across multidisciplinary audiences.

Nice To Haves

Experience applying machine learning to physical, interconnected networks.
Familiarity with commercial grid-simulation software or numerical solvers, such as PSS®E, GridLAB-D, or MATPOWER, alongside scientific Python tools.
A history of open-source contributions or peer-reviewed publications at leading AI conferences, such as NeurIPS, ICML, or ICLR, and/or power-systems conferences associated with the IEEE Power & Energy Society.
Experience operating in a startup, high-growth, or rapidly evolving technical environment.

Responsibilities

Own the technical roadmap and system architecture for Tapestry’s multimodal intelligence engines, scaling models across multimodal machine learning, graph neural networks, geospatial and remote-sensing data, reinforcement learning for physical control systems, and multi-turn agentic systems.
Partner closely with Tapestry’s machine learning technical leads, Power Systems Scientists, Software Engineers, Product Managers, and global utility partners to translate complex, large-scale grid data into actionable insights that improve grid planning, operations, and maintenance.
Serve as a technical force multiplier across the engineering organization by mentoring senior and staff-level engineers, establishing rigorous production standards, and aligning cross-functional stakeholders around architectural direction.
Advance the application of state-of-the-art AI architectures—including physics-informed neural networks and agentic AI—to solve highly constrained energy-infrastructure challenges in production environments.
Establish scalable architectural patterns and technical standards that improve the reliability, performance, and long-term maintainability of Tapestry’s machine learning systems.
Shape long-term machine learning strategy through first-principles thinking, rigorous technical analysis, and clear decision-making across complex and evolving problem spaces.