Jobs at X, The Moonshot Factory

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.

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.

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.

Develop advanced machine learning, neural reasoning, and simulation systems for climate sustainability, carbon markets, finance, and risk management. Build prototypes, optimization algorithms, data pipelines, and large-scale analyses using language and foundation models. Collaborate with experts across X, Alphabet, and academia to translate ambiguous problems into actionable insights, assess project economics, and support decarbonization initiatives. The residency runs four months to one year in Mountain View, California.

Design and build AI-powered enterprise products for the energy industry. Lead end-to-end product design across strategy, interaction, visual craft, prototyping, and implementation. Prototype in React, collaborate with product and engineering teams, influence product direction, and evolve production design systems. The role requires designing for complex technical users and may involve simulation, geospatial, data-intensive, visualization, and AI-powered products.

Lead strategic business development and partnership opportunities across global energy markets. Build relationships with utilities, energy providers, grid operators, and ecosystem partners; structure commercial agreements; define mutual success criteria; and advance early commercial engagements. Collaborate with product, engineering, and leadership to translate market needs into product priorities, analyze regulatory and competitive dynamics, track opportunities in CRM, and represent Tapestry at industry events.

Design and deploy AI-driven, closed-loop laboratory automation systems for biological and chemical discovery. Integrate laboratory hardware, robotics, firmware, APIs, sensors, and cloud AI infrastructure. Build data pipelines for high-throughput genomic, proteomic, and imaging data used in machine learning. Conduct wet-lab experiments, prototype technical features, translate experimental constraints across biology, chemistry, hardware, and machine learning teams, and communicate complex results clearly.

Conduct PhD-level research combining fluid dynamics, multiscale physics, and machine learning for materials discovery and manufacturing. Develop physics-informed, autodifferentiable architectures that replace computationally intensive forward solvers, parameterize unresolved physics, and scale SciML frameworks. Residents may work with laboratory instrumentation and physical experimentation, applying methods such as PINNs and Neural Operators to complex systems including turbulence, multiphase flow, and inverse design.

Strategic HR Business Partner focused on Compensation and Total Rewards: design compensation philosophy, lead annual compensation cycles, benchmarking, pay equity, build incentive programs and analytics, partner with Finance on workforce planning, advise leaders on org design, and scale rewards programs.

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.

Design and implement large-scale electric grid simulation and optimization algorithms. Collaborate with AI researchers, software engineers, and domain experts to develop high-performance computational tools for power systems modeling and production deployment, leveraging modern hardware and numerical methods.

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

Design and build developer productivity platforms and internal tools to enable fast, secure deployments. Improve inner-loop developer experience, standardize service templates, implement IaC and CI/CD pipelines, and provide observability, alerting, and APIs that let application teams manage production health independently.

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.

Design and build secure, scalable backend infrastructure and distributed services for Tapestry’s AI-powered electric grid platform. Develop large-scale data processing and modeling systems, collaborate with engineers, AI researchers, power systems experts, product managers, and external partners, and translate complex requirements into reliable production products.

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

Build, train, and deploy production ML models for the electric grid. Collaborate with ML engineers, data scientists, and software engineers across multimodal, NLP, retrieval, and agentic AI domains. Operationalize model training and serving at enterprise scale and stay current with ML research.

Tapestry invites candidates across engineering, product, operations, and energy systems to connect for future roles. Candidates should have experience in fast-paced, mission-driven environments, a passion for clean energy, and strengths in collaboration, ownership, and problem-solving. Submit a resume to be considered for upcoming opportunities.

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.