The **Synthetic Data Engineer** designs, builds, and operates systems that generate **privacy-preserving, high-utility synthetic datasets** that can be used for analytics, software testing, and machine learning development when direct use of production data is constrained by privacy, security, scarcity, or access limitations. This role sits at the intersection of **data engineering, generative modeling, and data privacy**—turning sensitive or hard-to-access datasets into governed synthetic alternatives that maintain statistical and downstream task fidelity.

The Staff Synthetic Data Engineer is a senior individual contributor in the AI & ML organization responsible for designing, building, and operationalizing synthetic data capabilities that accelerate model development while protecting privacy and enabling safer data sharing. This role blends advanced ML generative techniques with robust data engineering and governance to produce synthetic datasets that are statistically faithful, fit-for-purpose, and auditable.

The **Staff Robotics Software Engineer** is a senior individual contributor who designs, builds, and operationalizes core robotics software capabilities—typically spanning autonomy, motion/control interfaces, perception integration, simulation, and reliable on-robot runtime systems. The role balances deep hands-on engineering with cross-team technical leadership, ensuring robotics features are safe, performant, testable, and maintainable across real-world deployments.

The **Staff Responsible AI Engineer** is a senior individual contributor who designs, builds, and operationalizes technical systems that make AI products **safer, fairer, more transparent, privacy-preserving, and compliant**—at production scale. The role sits at the intersection of applied ML engineering, security/privacy engineering, governance, and product risk management, translating responsible AI principles into **measurable engineering requirements, controls, automated tests, and runtime safeguards**.

A **Staff Recommendation Systems Engineer** is a senior individual contributor who designs, builds, and continuously improves the end-to-end recommendation stack that powers personalized experiences (e.g., “For You” feeds, related items, search ranking, next-best-action, and content or product discovery). The role spans applied machine learning, large-scale data systems, online serving infrastructure, experimentation, and production reliability to deliver measurable product outcomes.

A **Staff RAG Engineer** designs, builds, and operates retrieval-augmented generation (RAG) systems that reliably ground large language model (LLM) outputs in enterprise data. The role sits at the intersection of applied ML, software engineering, information retrieval, and platform reliability—owning the end-to-end lifecycle from data ingestion and indexing through retrieval, prompting/orchestration, evaluation, and production operations.

The **Staff NLP Engineer** is a senior individual contributor (IC) responsible for designing, building, and operationalizing natural language processing (NLP) and large language model (LLM) capabilities that power customer-facing product experiences and internal intelligence workflows. This role owns the technical approach for complex language problems—such as search relevance, summarization, conversational interfaces, classification, and retrieval-augmented generation (RAG)—and ensures solutions meet enterprise standards for reliability, privacy, and cost.

The Staff MLOps Engineer is a senior individual contributor responsible for designing, scaling, and governing the end-to-end systems that reliably deliver machine learning (ML) models into production. This role bridges ML research/engineering and production-grade software operations by building standardized pipelines, model deployment patterns, observability, and controls that enable safe, repeatable, and fast iteration on ML-powered features.

The **Staff Machine Learning Engineer** is a senior individual contributor responsible for designing, building, and operating production-grade machine learning systems that deliver measurable product and business outcomes. This role bridges applied ML, software engineering, and platform thinking—ensuring models are not only accurate, but also reliable, scalable, observable, secure, and cost-effective in real-world usage.

The Staff LLM Engineer is a senior individual contributor in the AI & ML organization responsible for designing, building, and operationalizing Large Language Model (LLM) capabilities that are reliable, secure, cost-effective, and measurable in production. This role bridges applied research and production engineering—turning model and prompt experiments into scalable services, robust evaluation systems, and platform patterns that other teams can safely reuse.

The Staff Knowledge Graph Engineer designs, builds, and evolves enterprise-grade knowledge graph capabilities that connect fragmented data into a semantically consistent, queryable, and governable representation of the business. This role operates at Staff (senior technical leader) level, combining deep hands-on engineering with architecture, standards-setting, and cross-team enablement to deliver reliable graph-backed products and AI/ML features.