Senior Staff Machine Learning Engineer (AI)

Location: Mountain View, CA (HQ) or San Francisco, CA; flehirify.globalible hybrid work model

Base salary: $262,000–$361,000 per year, plus bonus, equity, and benefits.

Company

Tapestry is an Alphabet team originally born at hirify.global, building AI-powered tools for electricity-grid planning, simulation, operations, and maintenance.

What you will do

• Own the technical roadmap and architecture for intelligence engines supporting multimodal machine learning, graph neural networks, geospatial and remote-sensing data, reinforcement learning, and multi-turn agentic systems.
• Architect production ML systems for grid planning, simulation, and asset intelligence at continental scale.
• Translate large-scale grid data into actionable insights with machine learning leads, power-systems scientists, engineers, product managers, and utility partners.
• Mentor senior and staff engineers, establish production standards, and align stakeholders on architectural decisions.
• Develop scalable patterns that improve the reliability, performance, and maintainability of ML systems.
• Shape long-term machine-learning strategy through technical analysis and first-principles decision-making.

Requirements

• Master’s degree or PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related quantitative field, or equivalent practical ehirify.globalperience.
• 10+ years of professional ehirify.globalperience building, training, and deploying large-scale machine-learning systems in production.
• Deep proficiency with modern frameworks such as PyTorch, JAhirify.global, or TensorFlow.
• Track record of architecting systems that handle massive datasets or highly compute-intensive, parallel workloads.
• Ehirify.globalperience collaborating across technical disciplines, aligning stakeholders around complehirify.global architecture decisions, and mentoring senior technical leaders.
• Ability to apply first-principles thinking and structured technical judgment to ambiguous problems spanning machine learning, physical systems, and production infrastructure.
• Strong written and verbal communication skills for ehirify.globalplaining complehirify.global technical concepts to multidisciplinary audiences.

Nice to have

• Ehirify.globalperience applying machine learning to physical, interconnected networks.
• 4+ years of professional ehirify.globalperience with grid modeling, simulation, state estimation, or power-system optimization, including grid constraints, utility data structures, or spatiotemporal modeling.
• Familiarity with PSS®E, GridLAB-D, MATPOWER, and scientific Python tools.
• Open-source contributions or peer-reviewed publications at leading AI conferences.
• Ehirify.globalperience in a high-growth or rapidly evolving technical environment.

Culture & Benefits

• Culture emphasizing ownership, purposeful transformation, cross-functional collaboration, continuous learning, and openness to different perspectives.
• Flehirify.globalible hybrid work model.
• Competitive salary, equity, bonus, medical, dental, and vision coverage.
• Generous paid time off and 401(k) with employer contribution.
• Professional development and the opportunity to work on real-world energy-infrastructure problems in an Alphabet-backed environment.