Jobs at X, The Moonshot Factory

As a Staff ML Software Engineer, you will lead the development and deployment of machine learning applications, leveraging your expertise to drive software architecture decisions, maintain cloud infrastructure, and collaborate within a small, agile team focused on geospatial challenges related to climate change.

Lead cross-functional program execution for Tapestry's AI-powered electric grid: run agile ceremonies, manage sprints and backlogs, align engineering, product, and power systems teams, mitigate risks, track milestones, and drive programs from prototype to enterprise-scale commercialization.

The Partner Operations Manager supports moonshot teams by managing accounts and vendor relationships, improving operational processes, and facilitating project graduation.

The Business Innovator will lead strategy, conduct market analysis, build partnerships, and support investment readiness for moonshot projects at X.

Lead and grow a data science team to build production-grade ML solutions for electric grid visibility and resilience. Oversee data pipelines, annotation, experimental design (A/B tests), model evaluation, and cross-functional execution to drive product and business impact for power system partners.

Design and build a large-scale, secure Agentic Data Platform for ingestion, enrichment, streaming, and retrieval of multi-modal data. Implement low-latency streaming, microservices, governance and monitoring; collaborate with ML, product, and power-systems experts to integrate services and migrate teams with minimal downtime.

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.

Lead and grow the Agentic Data Platform team to transform raw grid data and imagery into a high-fidelity digital replica of electricity networks. Architect autonomous, LLM-driven data discovery and metadata enrichment systems, productionize ML models, ensure production reliability and data governance, and serve as a hands-on technical leader and NYC site anchor for hiring and culture.

Lead development of high-performance, accessible frontend web applications that visualize large geospatial and time-series energy datasets. Collaborate with UX and AI teams, own feature implementation, maintain component libraries, enforce testing and frontend standards, and translate product designs into scalable architectures.

Lead the product roadmap and lifecycle for grid simulation and algorithmic engines. Incubate research into production-ready computation features, prioritize and translate requirements, collaborate with engineering and PMs, and ensure performance, accuracy, and regulatory alignment for utility and grid partners.

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

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.

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.

Lead People infrastructure and analytics on GCP: own product strategy, data architecture, complex Google SQL queries, and build dashboards (Looker/Tableau). Write PRDs, design workflows, manage vendor security/compliance, integrate Generative AI, maintain systems and automations, triage technical backlog, and mentor one junior analyst.

Translate multi-modal AI research into 0-1 products: design and run ML experiments, build scalable training and evaluation infrastructure, implement MLOps and data pipelines, deploy models, and use AI code tooling to move experiments to production MVPs.

Join an AI residency to prototype ML and AI solutions for business process optimization, build data pipelines and large-scale analyses, collaborate cross-functionally, evaluate project value chains and risks, and engage with the resident community while advancing research on agentic systems and LLM-based workflows.

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

The role involves developing operations research models and algorithms while collaborating with project teams, intended for PhD candidates interested in machine learning applications.

Join an early-stage moonshot team to design, test, and characterize novel nanodevices. Build and optimize experimental setups, perform low-noise electrical measurements (wafer or packaged), analyze data, document procedures, present results, and collaborate with electrical, mechanical, and physics experts to improve device design and fabrication.