S e n i o r S t a f f M a c h i n e L e a r n i n g E n g i n e e r , T a p e s t r ySoftware Engineering Mountain View, CA (HQ), San Francisco, CA
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 across multimodal machine learning, agentic systems and ML/AI operations.You will architect machine learning systems that advance grid planning, simulation, and asset intelligence at continental scale.
How you will make 10X Impact - Own the technical roadmap and system architecture for Tapestry's 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 spanning agentic AI, physics informed ML models, natural language understanding and geospatial intelligence 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.
What you should have... - 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.
- 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.
It'd be great if you also had one or more of these: - Experience applying machine learning to physical, interconnected networks.
- 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.
- 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 .
- Experience operating in a high-growth, or rapidly evolving technical environment.
What we offer: A culture that supports growth, ownership, and meaningful impact, along with...
- Competitive salary and equity
- Medical, dental, and vision coverage
- Generous PTO and flexible hybrid work model
- 401(k) with employer contribution
- Professional development
- The ability to work on important real-world problems within an Alphabet-backed environment
The US base salary range for this full-time position is $262,000 - $361,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.
