Search Results for "Jenkins"

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.

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 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 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 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.

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 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.

A **Senior Synthetic Data Engineer** designs, builds, and operates production-grade synthetic data capabilities that enable teams to train, test, and validate AI/ML systems when real data is scarce, sensitive, biased, or costly to access. This role combines advanced data engineering with applied generative modeling, privacy engineering, and rigorous data quality evaluation to deliver synthetic datasets that are **fit-for-purpose**, **privacy-preserving**, and **operationally reliable**.

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.

The Senior MLOps Engineer designs, builds, and operates the systems and processes that reliably deliver machine learning models into production and keep them healthy over time. This role bridges ML development and production-grade engineering by creating automated, secure, observable, and cost-efficient pipelines for training, deployment, monitoring, and governance of models.

The **Senior Machine Learning Engineer** designs, builds, deploys, and operates production-grade machine learning systems that deliver measurable product and business outcomes. This role sits at the intersection of software engineering, applied machine learning, and data engineering, translating modeled insights into reliable services, pipelines, and platforms that can be monitored, governed, and improved over time.