Search Results for "Jenkins"

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.

The Senior LLM Engineer designs, builds, evaluates, and operates Large Language Model (LLM) capabilities that power user-facing product features and internal automation across a software or IT organization. This role turns ambiguous business needs (e.g., “make support faster,” “improve content quality,” “extract insights from documents”) into reliable, secure, cost-effective LLM systems that can be shipped and maintained in production.

The Senior Knowledge Graph Engineer designs, builds, and operates knowledge graph capabilities that turn fragmented enterprise data into a governed, queryable semantic layer powering AI-driven products and decisioning. This role owns key portions of the end-to-end lifecycle: ontology and schema design, entity resolution, ingestion and transformation pipelines, graph storage and indexing, and graph-aware APIs and analytics.

The **Senior Generative AI Engineer** designs, builds, and operates production-grade generative AI capabilities—typically LLM-powered applications, retrieval-augmented generation (RAG) systems, model-serving APIs, evaluation pipelines, and safety controls—that create measurable product and operational outcomes. This is a **senior individual contributor (IC)** role with end-to-end technical ownership across experimentation, engineering hardening, deployment, and lifecycle operations.

The **Senior Federated Learning Engineer** designs, builds, and operationalizes privacy-preserving machine learning systems that train across distributed data sources (devices, edge nodes, partner environments, or business units) without centralizing raw data. This role exists in software and IT organizations to unlock model performance and product intelligence while meeting rising privacy, data residency, and regulatory constraints.

The Senior Edge AI Engineer designs, optimizes, and operationalizes machine learning (ML) systems that run directly on edge devices (e.g., gateways, cameras, industrial controllers, mobile/embedded compute modules) where low latency, intermittent connectivity, privacy constraints, and hardware limitations shape the solution. This role translates model research and product requirements into production-grade on-device inference pipelines, including model compression, hardware acceleration, deployment automation, and fleet observability.

The **Senior Autonomous Systems Engineer** designs, builds, and validates autonomy capabilities that allow software-driven systems to perceive their environment, make decisions, and act safely with minimal human intervention. This role sits at the intersection of **AI/ML, robotics software, real-time systems, and safety engineering**, translating research-grade autonomy methods into reliable, testable, and deployable production software.

The **Senior Applied AI Engineer** designs, builds, and operates AI-powered product capabilities by turning research-grade approaches into **reliable, secure, scalable, and measurable** production systems. This role sits at the intersection of software engineering, machine learning, and data engineering, with a strong focus on **delivering user and business outcomes** rather than experimentation alone.

The **Senior AI Platform Engineer** designs, builds, and operates the internal platform capabilities that enable teams to reliably develop, train, evaluate, deploy, and monitor machine learning (ML) and generative AI (GenAI) systems at scale. The role balances strong software engineering and cloud infrastructure skills with pragmatic MLOps practices, focusing on repeatability, security, cost efficiency, and developer experience for data scientists and ML engineers.

The Senior AI Evaluation Engineer designs, implements, and operationalizes robust evaluation systems to measure the quality, safety, reliability, and business performance of AI models—especially modern ML and LLM-based capabilities—throughout the development lifecycle and in production. The role translates ambiguous “model quality” questions into measurable metrics, repeatable test suites, and automated gates that prevent regressions and enable responsible scaling of AI features.

The Senior AI Engineer designs, builds, deploys, and operates production-grade machine learning (ML) and generative AI capabilities that deliver measurable business outcomes in a software or IT organization. This role bridges applied research and software engineering by translating problem statements into reliable model-powered services, data/feature pipelines, evaluation frameworks, and scalable inference architectures.

A **Robotics Software Engineer** designs, builds, tests, and deploys software that enables robots to perceive their environment, make decisions, and act reliably in the physical world. In a software or IT organization—especially within an **AI & ML** department—this role bridges machine learning, real-time systems, and production-grade software engineering to deliver robotic capabilities as a product, platform, or internal capability.

A Retrieval Engineer designs, builds, and operates the retrieval layer that selects the best candidate information for downstream AI systems (e.g., RAG applications, search experiences, recommendations, and ranking pipelines). The role focuses on indexing strategies, query understanding, hybrid retrieval (lexical + vector), relevance evaluation, and performance engineering so that the right content is fetched reliably, safely, and at low latency.

A **RAG Engineer** designs, builds, and operates **Retrieval-Augmented Generation (RAG)** systems that connect large language models (LLMs) to enterprise knowledge—enabling accurate, grounded, and secure answers inside products and internal tools. This role exists because LLMs alone are not sufficient for most enterprise use cases: the business needs **fresh, permissioned, auditable, and domain-specific** responses backed by trusted sources and measurable performance.

The **Principal Synthetic Data Engineer** is a senior individual contributor (IC) responsible for designing, building, and governing enterprise-grade synthetic data capabilities that accelerate AI/ML development while reducing privacy, security, and data access constraints. This role combines deep data engineering and ML knowledge with rigorous privacy/utility evaluation to produce synthetic datasets that are fit-for-purpose for model training, testing, analytics, and product experimentation.

The **Principal Robotics Software Engineer** is a senior individual-contributor (IC) technical leader responsible for designing, building, and evolving the software foundations that enable reliable robotic autonomy at scale—typically across perception, localization, planning, control, and the runtime platform that orchestrates these capabilities. The role blends deep robotics engineering expertise with software architecture, production-grade quality practices, and cross-functional technical leadership across AI & ML, product, and operations.

The Principal RAG Engineer is a senior individual contributor responsible for designing, building, and operating Retrieval-Augmented Generation (RAG) systems that deliver reliable, secure, and high-quality AI experiences in production. This role blends applied ML engineering, search/retrieval engineering, distributed systems, and software architecture to ensure LLM-based products are grounded in trusted enterprise knowledge and perform predictably at scale.

The Principal MLOps Engineer is a senior individual contributor responsible for designing, standardizing, and scaling the end-to-end systems that reliably deliver machine learning models into production. This role bridges ML engineering, data engineering, DevOps/SRE, and security to ensure models are deployable, observable, governed, cost-efficient, and continuously improving.