Jobs at X, The Moonshot Factory

Lead product strategy and execution for a scalable, secure data platform powering energy-grid AI use cases. Define versioning/collaboration, data lineage and metadata, privacy and zero-trust controls, build-vs-buy decisions, and enable internal teams and global partners to discover and use high-fidelity energy data.

Optimize and maintain high-performance C++ runtime pipelines for on-device ML on edge GPUs. Profile and reduce latency, refine host-to-device streaming and memory use, deploy and tune TensorRT/CUDA/ONNX runtimes, and collaborate with ML engineers to integrate multi-sensor inputs into low-latency inference loops.

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.

Lead and grow a global engineering team building resilient, cloud-based distributed systems for the electric grid. Drive technical strategy, architecture, hiring, and cross-functional collaboration with Product, UX, Data Science, and Power Systems to deliver large-scale data processing, backend services, and frontend solutions for global deployments.

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.

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

Lead and organize early-stage geoscience AI initiatives: coordinate workstreams, manage partners, define metrics, run experiments, document progress, and act as the GM proxy to remove roadblocks and scale prototypes into products.

Lead and grow a data science team focused on electric grid visibility and resilience. Oversee data curation, annotation, and quality control; define metrics and experimentation frameworks; translate business goals into data science roadmaps; deliver production-grade ML evaluation tooling; and communicate strategic recommendations to executives while collaborating with engineering, product, and power systems experts.

Design front-end experiences for next-generation AI models and agents at early product stage. Conceptualize interfaces beyond chat, build rapid prototypes and wireframes, collaborate with engineers and researchers, conduct user research and usability testing, and iterate designs based on data and technical constraints in a fast-moving environment.

Lead technical delivery of Tapestry products to utilities by ingesting, cleaning, and transforming large-scale grid datasets, resolving topology and powerflow modeling discrepancies, conducting technical customer screenings, training users, and feeding deployment learnings into product roadmap and playbooks to accelerate adoption.

Develop and improve C++ systems interacting with hardware sensors, build scalable data pipelines, and design deep learning algorithms for chemical data insights.

The Machine Learning Engineer will design and deploy multi-modal models while collaborating with cross-functional teams to translate research into production-ready solutions, focusing on climate change mitigation.

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.

Design, build, and scale HR operational systems and workflows for hiring, onboarding, and employee lifecycle. Own day-to-day HR operations (onboarding, exits, transfers), maintain data pipelines and Apps Script dashboards, run data analysis and visualizations, partner with Talent Acquisition, HR, and leadership to improve processes and candidate/employee experience, and automate workflows using scripting and AI-assisted tools.

Act as the domain expert linking power systems engineering, product, and engineering teams to build scalable planning workflows. Define technical specs, prototype and QA power system analyses, translate regulatory and RTO/ISO requirements into product features, and synthesize partner feedback and grid data to refine platform capabilities for large-load interconnection and planning.

Lead end-to-end grid model strategy by converting multi-source industry grid data into CIM, ensuring model QA, running electrical validations, staging validated models into applications, and coordinating UAT with product, engineering, and external partners.

Architect and own the ML roadmap for continental-scale grid intelligence. Design and deploy multimodal ML, GNNs, physics-informed models, and RL for grid planning, simulation, and operations. Mentor senior engineers, set production standards, and collaborate with power-systems scientists and utility partners to deliver scalable, reliable ML systems.