Content Management Specialist Jobs

Senior DevSecOps Software Engineer Company: The Boeing Company The Boeing Company has an exciting opportunity for a Senior DevSecOps Software Engineer to support the ADaPS organization in Huntsville, AL. ADaPS (an organization of over 100 software engineers) supports programs across the entire portfolio of Boeing products including Space Launch Systems (SLS) Upper Stage, Satellite Efforts, and Advanced Missile Defense efforts including Patriot Advanced Capability (PAC3). ADaPS specializes in implementing rapid prototyping and development programs using small, adaptable agile teams. Position Responsibilities: Oversees establishment and maintenance of modern software development environments, with formalized processes for development, implementation, integration, testing, documenting, and deploying software changes Works collaboratively with software engineering teams to deploy and operate systems Oversees development and maintenance of continuous integration / continuous delivery (CI/CD) pipelines Oversees automation and streamlining of operations and processes that support software testing, including script development as needed Builds and maintains tools for deployment, monitoring, and operations Communicates technical content to stakeholders Analyzes, plans, and executes mitigation strategies to prevent potential security risks, threats, and vulnerabilities Troubleshoots and resolves issues in development, testing, and production environments Supports software development and testing operations. Performs software research and trade studies Security Clearance: This position requires the ability to obtain a U.S. Se

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Responsibilities and Tasks: Develop and refine CMP processes to deliver outstanding performance and yield. Collaborate with cross-functional teams to successfully implement process improvements. Conduct rigorous data analysis to determine the root cause of process variations and implement corrective actions. Ensure all processes strictly align with industry standards and safety regulations. AI Relevant Job Responsibilities: - Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements. - Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work. - Ability to apply baseline digital fluency and role‑appropriate AI literacy to use AI‑enabled tools responsibly and effectively for research, analysis, content creation, problem‑solving, operational tasks, and achieving business About Micron Technology, Inc. We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all . With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM,

About Us Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X , and discover the projects we’re solving on our Blog . Be sure to explore our Interviewing Guide to learn how to ace our interview process. About the Role The Community Originals team at Twitch is responsible for producing some of the most unique content in livestreaming. We're currently seeking a Sr. Programming & Development Manager to help lead our

About Us Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day. We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X , and discover the projects we’re solving on our Blog . Be sure to explore our Interviewing Guide to learn how to ace our interview process. About the Role The Community Originals team at Twitch is responsible for producing some of the most unique content in livestreaming. We're currently seeking a Sr. Programming & Development Manager to help lead our

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses. As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic. The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack. What You Will Be Doing Own the production improvement loop for Context Engine: underst

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses. As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic. The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack. What You Will Be Doing Own the production improvement loop for Context Engine: underst

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses. As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic. The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack. What You Will Be Doing Own the production improvement loop for Context Engine: underst

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses. As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic. The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack. What You Will Be Doing Own the production improvement loop for Context Engine: underst

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses. As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic. The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack. What You Will Be Doing Own the production improvement loop for Context Engine: underst

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role The Context Engine team builds the knowledge layer that AI agents use to work with enterprise data in Elasticsearch. We extract knowledge from any data sources into a structured AI Index, serve it to agents through public APIs, MCP tools and framework integrations, and close the loop with agent traces so that what the engine knows improves from real usage. Any agent can use it: Elastic’s own Agent Builder, Claude Code, LangChain and other third-party harnesses. As a Principal AI Engineer, you own the improvement loop of this product end to end: how agents, automations and skills behave in production, how we observe them, how we evaluate them, and how we ship changes to them safely. This is a hybrid role at the intersection of engineering, data science, and product. You will write production code, design evaluation and telemetry that product decisions can rest on, and set the technical bar for how the team iterates on agentic behaviour. You will work alongside data scientists, backend engineers, product, and UX, and your work will show up directly in what customers build on top of Elastic. The codebase is TypeScript and we build it in the open, so you'll be shipping code, designs and discussions in public alongside the rest of the Elastic Stack. What You Will Be Doing Own the production improvement loop for Context Engine: underst

Celonis is the trusted platform to industrialize Enterprise AI. At our core is the Celonis Context Model — which combines process data, business knowledge, and intelligence into a living digital twin of the enterprise that AI can actually understand, turning AI's operational blind spots into operational clarity. World's leading companies trust Celonis and its global ecosystem of partners to make their AI agents, people, systems, and processes work together, achieving transformational outcomes. We believe there's a massive opportunity to unlock global productivity and sustainability by giving AI the context to understand how every business process really works. Join our mission to make processes work for people, companies, and the planet. The Team: The Celonis Context Model provides a real-time digital twin of enterprise operations, serving as the bridge between enterprise systems and AI. It captures how business actually flows across systems, giving organizations and AI agents the context needed to optimize complex workflows in real time. As an engineer on the Context Model Core team, you will shape the foundation powering the future of Enterprise AI and Process Intelligence. The Role: We are looking for a Full Stack Engineer to build the core capabilities to power the Celonis Context Model. In this role, you will build the next generation of user-facing tools and robust backend services that define how the world’s largest enterprises understand their data. You will tackle full-stack engineering challenges end-to-end: from designing scalable microservices in Java (Spring Boot) to building rich, interactive UI components in Angular and TypeScript . You’ll work closely with product and design to turn abstract data modeling into fast, reliable and joyful user experiences. Your work at Celonis will directly impact companies and people around the globe as they improve their processes to save money, reduce waste, improve automation and generally become more efficien

Job Requisition ID # 26WD101097 Autodesk Research – Intelligent Construction Summer 2027 | 12 weeks Position Overview Modern AI systems increasingly depend on their ability to access and use the right context at the right time. An important research challenge is how large, evolving bodies of information should be represented to support reliable inference. Autodesk Research provides a distinctive setting for this work. Construction projects are rich, complex information environments whose state changes continuously over time, making them a compelling domain for studying context and knowledge systems for AI. As a Research Intern, you will own a focused research problem in this area. You will investigate new approaches to building and retrieving structured context, develop experimental prototypes, and evaluate how effectively they support downstream AI reasoning. You will work closely with Autodesk researchers and engineers and have substantial freedom to bring ideas from your own research. Strong contributions may form the basis of an academic publication and future Autodesk research. Responsibilities • Conduct original research in context and knowledge systems for AI. Investigate methods for representing and retrieving information to support reliable contextual inference • Develop new approaches to context construction. Explore how relevant knowledge can be identified and assembled dynamically from large and evolving information environments • Build research prototypes. Implement experimental systems using graph-based

We're looking for an Engineering Manager to lead the Agent Context team in NYC. This team owns how Asana's AI systems search, retrieve, and reason over the work graph the search infrastructure, dense embedding pipelines, ranking systems, and evaluation frameworks that determine whether every AI experience at Asana is trustworthy or not. Your mission is to make retrieval comprehensive, reliable, and fast at enterprise scale, positioning Asana as the coordination and memory layer for the agentic enterprise. This is a high-leverage platform role: nearly every AI product at Asana - AI Teammates, chat experiences, agentic workflows depends on the systems this team builds. You will manage a team of senior engineers in New York collaborating daily with partner teams in San Francisco and Warsaw, and work alongside a dedicated Product Manager as your direct counterpart. This role is based in our New York office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday; most Asanas have the option to work from home on Wednesdays. What you'll achieve Own the technical direction and delivery of Asana's retrieval stack end to end: lexical and semantic search, dense embedding generation and backfill at scale, chunking and ranking strategies, and RAG comprehensiveness across the work graph. Build and operate the evaluation infrastructure that makes retrieval quality measurable recall/precision benchmarks, offline and online evals, and comparative testing across retrieval backends - so quality decisions are made with data, not vibes. Drive the cost, performance, and quality tradeoffs that define this space: when semantic search earns its infrastructure cost over lexical, how to hit latency targets without sacrificing recall, and how retrieval improvements compound into cheaper, faster downstream LLM calls. Set and enforce the bar for how other teams at Asana integrate with retrieval: clear ownership of embedding decisions, rollout g

About the Team The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. About the Role We believe that the final enabler for AGI is spending compute on context. As a Context Researcher on Agent Post-Training, you will scale compute spent on context. You will get to work in our frontier training stack on enabling the next paradigm of model training with a clear product interface for iterative deployment (Codex Chronicle). You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. In this role, you might Design and run experiments that improve scaling of compute on context. Own end-to-end improvements to the post-training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model-behavior analysis. Build evals and environments that expose the next set of model failures,

About us Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor and Clay Bavor . Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. What you'll do You’ll join a full-stack data team building the systems making our AI agents measurably smarter with every interaction, building the real-time pipelines, analytics products, and deep personalization primitives that turn millions of conversations into business outcomes. You’ll work across these areas: Data Foundations: Architect and build our core platform to handle data at scale with low latency. This includes our real-time eventing infrastructure, streaming and batch ETL pipelines, and our Iceberg-based data lakehouse. You will own the systems for interactive OLAP querying, orchestration, and experimentation, ensuring our data is trustworthy, fast, and easy to use for the entire organization. Memory & Personalization: Power the next gene