Content Manager Jobs

At Linear, we're building the product development system for teams and agents. AI is fundamentally changing how software gets built, and we’re shaping the tools this new era requires. Founded in 2019, Linear has become the platform of choice for more than 40,000 companies (including OpenAI, Coinbase, and Ramp) to plan, build, and ship their products. Today, our team is distributed across North America, Europe, and Australia, and we’re continuing to grow internationally. What unites us is relentless focus, fast execution, and a deep care for software craftsmanship. As a small team, we’re all generalists that work across the full stack (built in Typescript end-to-end). We’re looking for experienced engineers that thrive in an environment of autonomy and individual responsibility to help us build the future of product development. Location & work mode Linear is a remote-first company, with optional co-working offices in San Francisco, New York, and London. This role is open to candidates based in the US and Europe. You can work from anywhere within those regions. We value deep focus and async collaboration, with intentional moments to connect in person through team off-sites, optional co-working, and occasional travel. What you'll do Build new user-facing features with everything from database models to GraphQL resolvers and UI components Optimize our data synchronization stack by applying better serialization protocols Add real-time collaborative editing to our content editor Improve performance by profiling and tweaking virtualized list rendering Add analytics, monitoring, and alerts to our service so that we can better respond to operational incidents Open-source any non-trivial innovations that come out of our work on the product Redefine best-in-class software development processes so that we can build a purpose-built product. What we're looking for 5+ years of experience building customer-facing products at a high-quality software company Strong React and Typ

At Linear, we're building the product development system for teams and agents. AI is fundamentally changing how software gets built, and we’re shaping the tools this new era requires. Founded in 2019, Linear has become the platform of choice for more than 40,000 companies (including OpenAI, Coinbase, and Ramp) to plan, build, and ship their products. Today, our team is distributed across North America, Europe, and Australia, and we’re continuing to grow internationally. What unites us is relentless focus, fast execution, and a deep care for software craftsmanship. As a small team, we’re all generalists and constantly picking up new challenges. When it comes to code, we’re looking to work with experienced people who can pick a problem and solve it. We use TypeScript and build scalable systems so we can continuously make progress on a solid foundation. We don’t expect you to have a background in everything we use, but we do expect strong JavaScript fundamentals and a background working with React and TypeScript. Location & work mode Linear is a remote-first company, with optional co-working offices in San Francisco, New York, and London. This role is open to candidates based in North America and Europe. You can work from most timezones within these regions. We value deep focus and async collaboration, with intentional moments to connect in person through team off-sites, optional co-working, and occasional travel. What you'll do Build new user-facing features with everything from database models to GraphQL resolvers and UI components Optimize our data synchronization stack by applying better serialization protocols Add real-time collaborative editing to our content editor Improve performance by profiling and tweaking virtualized list rendering Add analytics, monitoring, and alerts to our service so that we can better respond to operational incidents Open-source any non-trivial innovations that come out of our work on the product Redefine best-in-class software deve

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,

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

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 PostHog PostHog is your product’s context layer. It ingests and stores your analytics, errors, replays, and business data so you and your agents can query and act on it. We launched out of Y Combinator's W20 cohort . We've since shipped more than a dozen products, including: A suite of analytics and observability tools, including product analytics , replay vision , AI observability , and error tracking PostHog MCP for users and their agents to query and act on their product and business data Data warehouse so users can query product and customer data together using custom SQL insights. We are: Product-led . More than 500K organizations have installed PostHog, mostly driven by word-of-mouth. We have intensely strong product-market fit. Default alive . Revenue is growing incredibly quickly, and we're very efficient. We raise money to push ambition and grow faster, not to keep the lights on. Well-funded. We've raised more than $180m from some of the world's top investors. We're set up for a long, ambitious journey. We're focused on building an awesome product for end users, hiring exceptional teammates, shipping fast, and being as weird as possible . Things we care about Transparency: Everyone can read about our roadmap, how we pay (or even let go of) people, our strategy, and how we work, in our public company handbook . Internally, we share revenue, notes and slides from board meetings, and fundraising plans, so everyone has the context they need to make good decisions. Autonomy: We don’t tell anyone what to do. Everyone chooses what to work on next based on what's going to have the biggest impact on our customers, and what they find interesting and motivating to work on. Engineers lead product teams and make product decisions . Teams are flexible and easy to change when needed. Shipping fast: Why not now? We want to build a lot of products; we can't do that shipping at a normal pace. We've built the company around small teams – autonomous, highly-efficient gro

About PostHog PostHog is your product’s context layer. It ingests and stores your analytics, errors, replays, and business data so you and your agents can query and act on it. We launched out of Y Combinator's W20 cohort . We've since shipped more than a dozen products, including: A suite of analytics and observability tools, including product analytics , replay vision , AI observability , and error tracking PostHog MCP for users and their agents to query and act on their product and business data Data warehouse (existing text is fine) We are: Product-led . More than 500K organizations have installed PostHog, mostly driven by word-of-mouth. We have intensely strong product-market fit. Default alive . Revenue is growing incredibly quickly, and we're very efficient. We raise money to push ambition and grow faster, not to keep the lights on. Well-funded. We've raised more than $180m from some of the world's top investors. We're set up for a long, ambitious journey. We're focused on building an awesome product for end users, hiring exceptional teammates, shipping fast, and being as weird as possible . Things we care about Transparency: Everyone can read about our roadmap, how we pay (or even let go of) people, our strategy, and how we work, in our public company handbook . Internally, we share revenue, notes and slides from board meetings, and fundraising plans, so everyone has the context they need to make good decisions. Autonomy: We don’t tell anyone what to do. Everyone chooses what to work on next based on what's going to have the biggest impact on our customers, and what they find interesting and motivating to work on. Engineers lead product teams and make product decisions . Teams are flexible and easy to change when needed. Shipping fast: Why not now? We want to build a lot of products; we can't do that shipping at a normal pace. We've built the company around small teams – autonomous, highly-efficient groups of cracked engineers who can outship much larger comp