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About This Role
Software EngineeringMountain View, CA (HQ)
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.
How you will make 10X Impact
- 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.
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.
- 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.
It’d be great if you also had one or more of these:
- 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.
Tapestry Values
- Take charge: We take initiative and own outcomes that move the mission forward.
- Transform with purpose: We build solutions that solve real problems and create meaningful impact.
- Be a Tapestry, not a thread: We collaborate across diverse skills and perspectives to achieve more than we can individually.
- Always fine\-tune: We stay curious, seek feedback, and refine our understanding as we learn.
- Stay grounded: We listen openly, value different perspectives, and stay focused on what matters most.
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.
An Equal Opportunity Workplace
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At X, we don't just accept difference \- we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.
If you have a disability or special need that requires accommodation, please contact us at x\-accommodation\-request@x.team.
Salary Context
This $262K-$361K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At X Development, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($311K) sits 42% above the category median. Disclosed range: $262K to $361K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
X Development AI Hiring
X Development has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $304K - $361K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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