Jobs at X, The Moonshot Factory

Own the end-to-end architecture and multi-year technical roadmap for a planetary-scale geospatial, data, ML, and cloud platform. Design petabyte-scale pipelines, production MLOps and agentic systems, secure multi-tenant APIs, and reliable enterprise products. Establish technical standards for scalability, observability, security, disaster recovery, and cost efficiency. Remain hands-on through prototyping, critical-path coding, design reviews, and technical problem solving while mentoring senior engineers and influencing cross-functional leaders.

Build and ship AI tools used across X’s moonshot factory. Identify organizational workflow opportunities, prototype AI solutions, gather user feedback, drive adoption, and collaborate with engineering and research teams to develop effective internal products.

Build and deploy production machine learning systems across experimentation, architecture, MLOps, and infrastructure. Develop continual-learning pipelines, agentic ML systems, observability, explainability, monitoring, retraining, and drift detection. Manage Google Cloud-based ML infrastructure and high-volume production applications while collaborating with technical and external stakeholders. Work with geospatial and Earth observation data, iterative Agile development, and systems that evolve from prototypes into customer-facing products.

Design and implement secure, reliable, scalable backend infrastructure and services for the Tapestry energy platform. Develop data ingestion, storage, and enterprise applications; collaborate with engineers, scientists, power systems experts, product managers, and external partners; guide technical strategy and architecture; troubleshoot production systems; and mentor engineers while enabling faster, safer product delivery.

Lead the technical direction and architecture of infrastructure and developer productivity platforms. Build and operate multi-tenant Kubernetes environments on GCP, scalable compute for CPU, GPU, and TPU workloads, automated CI/CD pipelines, infrastructure-as-code systems, secure AI sandboxes, and observability standards. Mentor engineers, guide design reviews, establish reliability practices, and deliver self-service tools that improve developer velocity, deployment frequency, and recovery times.

Lead Tapestry’s global brand and communications strategy, shaping its narrative, positioning, reputation, executive voice, stakeholder communications, and internal communications. Advise the CEO and leadership team, translate complex AI and electric-grid technology into compelling stories, engage modern media channels, and manage high-stakes communications involving privacy, security, regulation, and other sensitive issues. This hands-on player-coach role partners across Product, Engineering, Commercial, Policy, Alphabet, customers, policymakers, and employees.

Lead the architecture, deployment, and operation of scalable multi-region cloud infrastructure across multiple providers. Build internal developer platforms, automate CI/CD and cloud operations, implement observability, security, reliability, incident response, and FinOps practices. Establish SLOs, optimize costs, manage production incidents, mentor engineering teams, and guide long-term infrastructure roadmaps. The role also supports high-throughput systems, distributed architectures, and potentially geospatial and geo-ML workflows.

Leads the technical direction and implementation of AI systems for global supply chain applications. Builds production-grade solutions across data pipelines, machine learning, LLMs, agentic AI, software architecture, deployment, and monitoring. Translates AI research into enterprise products, integrates legacy systems and cloud infrastructure, ensures responsible AI practices, and mentors engineers while guiding technical initiatives in a fast-paced startup environment.

PhD residents will develop AI-for-science systems, including multi-agent research automation and LLM fine-tuning for unstructured data analysis. Responsibilities include building AI workflows, processing large heterogeneous datasets, integrating data from varied sources, evaluating models rigorously, and applying time-series, statistical, graph, causal, and Bayesian methods. The program lasts four months to one year and is based at X headquarters in Mountain View, California.

Build and maintain MLOps infrastructure: CI/CD pipelines, containerize and deploy ML models, manage training and batch prediction workflows, and ensure scalable, highly available production ML systems while collaborating with researchers and data scientists.

Own end-to-end deployment of enterprise AI products for global supply chain customers. Build customer-specific software extensions, APIs, bidirectional data pipelines, integrations, and agentic workflows. Partner with clients, end-users, and IT teams to translate operational needs into scalable engineering solutions, overcome technical and adoption barriers, and guide platform evolution. Codify field solutions into reusable deployment playbooks and software components.

Identifies and evaluates high-impact moonshot opportunities through critical thinking, research, networking, prototyping, and technical and business assessments. De-risks ideas, leads small technical teams, develops proofs of concept, and communicates compelling opportunity narratives to leadership. The role requires extensive frontier-industry leadership, broad technical expertise, entrepreneurial judgment, and experience building viable businesses amid extreme ambiguity.

Lead the vision, roadmap, and evolution of Bellwether’s AI-powered platform for physical climate risk. Own model, agent, and platform components while translating customer needs across insurance, capital markets, government, and resilience sectors into workflows and features. Partner with engineering, research, design, and external stakeholders to manage discovery, prioritization, releases, documentation, adoption, and product strategy. The role requires deep technical understanding of AI systems, data infrastructure, cloud platforms, and enterprise product management.

Supports executives and team operations through complex calendar management, travel, facilities, vendors, office administration, events, onboarding, and cross-functional process improvement. The role partners with leadership and HR, manages workplace logistics, coordinates meetings and offsites, improves workflows, and helps maintain an organized, productive, and scalable environment.

Bridge legal and engineering teams to evaluate early-stage technologies, identify patentable innovations, conduct IP landscaping and freedom-to-operate screening, and develop strategic global patent portfolios. Lead invention harvesting, technical trainings, disclosure management, and collaboration with outside counsel. Support AI-driven patent discovery tooling, white-space analysis, and prompt repositories while translating complex technical concepts into actionable IP strategies.

Leads technical strategy, architecture, and development for enterprise applications supporting the electric grid at large scale. Guides distributed systems design, reliability, security, performance, and operational excellence. Partners with product, design, data science, and power systems teams to convert ambiguity into technical roadmaps. Provides hands-on technical guidance, mentors engineers and technical leaders, and builds high-performing engineering teams and organizational capabilities.

The role involves developing full-stack AI enterprise software, from design to deployment, mentoring teammates, and integrating AI into software environments.

Build and consolidate data infrastructure for ML training: design automated ETL/ELT pipelines, implement DataOps practices (validation, monitoring, anomaly detection), integrate annotation workflows, manage dataset versioning and storage, and collaborate with ML engineers and operations to produce high-quality, reproducible training datasets.

The Research Engineer will develop and deploy machine learning applications focused on severe weather, collaborating on system processes, and maintaining production environments.

This role involves leading innovation projects at X, developing breakthrough technologies from inception to product launch, and creating external partnerships.