Search Results for: SRE

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.

The **Staff Generative AI Engineer** is a senior individual contributor who designs, builds, and operationalizes generative AI (GenAI) capabilities—typically LLM-powered products, platform services, and internal developer enablement—at enterprise production standards. This role bridges advanced ML/LLM engineering with software architecture, reliability, security, and responsible AI governance to deliver scalable, measurable business value rather than isolated demos.

The **Staff Federated Learning Engineer** is a senior individual contributor responsible for designing, building, and operationalizing federated learning (FL) systems that train and improve machine learning models across distributed data sources without centralizing sensitive data. This role turns privacy-preserving ML research into reliable, scalable production capabilities—spanning edge devices, customer tenants, and regulated environments—while maintaining strong security, performance, and model quality.

The **Staff Edge AI Engineer** is a senior individual contributor who designs, builds, and operationalizes machine learning inference systems that run reliably on **resource-constrained, privacy-sensitive, and latency-critical edge environments** (e.g., mobile, IoT gateways, cameras, industrial devices, and on-prem appliances). The role bridges applied ML, systems engineering, and platform thinking to ensure models are **deployable, observable, secure, and maintainable** outside the data center.

A **Staff Computer Vision Engineer** is a senior individual contributor who designs, builds, and operationalizes computer vision (CV) systems that reliably perform in real-world production environments. The role blends deep model and algorithm expertise with strong software engineering and systems thinking to deliver vision capabilities (detection, segmentation, OCR, tracking, pose/geometry, multimodal vision-language components) that meet product requirements for accuracy, latency, cost, and safety.

The **Staff Autonomous Systems Engineer** designs, builds, and operationalizes the core software and ML-driven capabilities that enable machines or software agents to perceive their environment, make decisions, and act safely and reliably with minimal human intervention. This role sits at the intersection of **robotics/autonomy algorithms, production-grade software engineering, and ML systems**, with a strong emphasis on safety, validation, and real-world performance.

The **Staff Applied AI Engineer** is a senior individual contributor who designs, builds, and productionizes AI/ML capabilities that deliver measurable product and operational outcomes. This role bridges research-grade modeling and enterprise-grade software engineering by translating business problems into reliable, scalable, observable AI systems integrated into customer-facing and internal products.

The **Staff AI Safety Engineer** is a senior individual contributor in the AI & ML organization responsible for **engineering, operationalizing, and continuously improving safety controls** for AI systems—especially large language model (LLM) and generative AI capabilities—across the product lifecycle. This role ensures that AI-enabled features are **safe, reliable, compliant, and aligned with company policy**, while still supporting product velocity and customer value.

The **Staff AI Platform Engineer** designs, builds, and operationalizes the internal platforms, services, and paved roads that enable product and data teams to safely develop, deploy, monitor, and continuously improve machine learning (ML) and generative AI (GenAI) systems at scale. This is a senior individual contributor (IC) role with broad technical scope, meaningful architectural decision rights, and strong cross-functional influence across AI/ML, infrastructure, security, and product engineering.

The Staff AI Evaluation Engineer designs, builds, and operationalizes the evaluation systems that determine whether AI models and AI-powered product features are *good enough to ship* and *safe enough to scale*. This role creates the measurement “truth” for AI quality by defining metrics, building test suites and automated evaluation pipelines, running human and automated grading programs, and connecting offline results to online product outcomes.

The **Staff AI Engineer** is a senior individual contributor responsible for designing, delivering, and operating production-grade AI/ML capabilities that create measurable product and platform outcomes. This role sits at the intersection of applied machine learning, software engineering, and platform reliability—turning models, data, and experiments into secure, observable, cost-effective services that scale.

The **Staff AI Agent Engineer** designs, builds, and operationalizes AI agents that can reliably execute multi-step tasks using large language models (LLMs), tools/APIs, retrieval systems, and workflow orchestration. This role sits at the intersection of software engineering, applied ML, and platform reliability—owning agent architecture, evaluation, safety guardrails, and production readiness across multiple product surfaces.

The **Senior Robotics Software Engineer** designs, builds, and operates production-grade robotics software systems that run reliably on real robots and in high-fidelity simulation. This role sits at the intersection of software engineering excellence, AI/ML-driven autonomy, real-time systems, and rigorous validation, delivering robotics capabilities as scalable software components and platforms.

The **Senior Responsible AI Engineer** designs, implements, and operationalizes technical controls that make AI systems safer, fairer, more transparent, privacy-preserving, and compliant across the AI lifecycle—from data ingestion and model training through deployment, monitoring, and incident response. This role blends strong software engineering and MLOps practices with applied Responsible AI (RAI) methods (e.g., fairness evaluation, explainability, privacy, robustness, and governance-by-design).

The **Senior Recommendation Systems Engineer** designs, builds, and optimizes large-scale recommendation and ranking systems that personalize user experiences across product surfaces (e.g., home feed, “for you,” related items, search suggestions, notifications, email, and merchandising placements). This role blends applied machine learning, distributed systems, and experimentation rigor to deliver measurable improvements in engagement, conversion, retention, and user satisfaction.

The **Senior RAG Engineer** designs, builds, and operates **retrieval-augmented generation (RAG)** systems that connect large language models (LLMs) to enterprise knowledge and product data—safely, reliably, and cost-effectively. The role exists to move LLM use cases from prototypes to **production-grade AI capabilities** with measurable quality (groundedness, relevance, accuracy), robust governance, and operational excellence.

The Senior Prompt Engineer designs, tests, deploys, and continuously improves prompt-driven behaviors for large language model (LLM) features used in production software products and internal platforms. The role translates ambiguous business intent into reliable, safe, and measurable model interactions—often combining prompting techniques with retrieval, tool-use/function calling, structured outputs, and evaluation harnesses.