Jobs at X, The Moonshot Factory

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.

Leads and mentors a data science team building production-grade solutions for electric grid visibility and resilience. Oversees data pipelines, curation, annotation quality, metrics, experimentation, model evaluation, and advanced analytics. Partners with engineering, product, power systems experts, executives, and external stakeholders to define strategy, communicate insights, and deliver ML-powered products using rigorous statistical and engineering practices.

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.

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 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.