Content Writer Jobs

About Ramp Ramp is building the smart infrastructure for finance teams, embedded in the transaction flow of every dollar a business spends. We automate how over $200B in annualized spend flows in and out of 70,000+ companies: authorizing payments, flagging risk, categorizing spend, and closing books. The problems are high-stakes, data-dense, and unforgiving. We hire people with high agency and high urgency. We look for slope over intercept. We care less about where you trained and more about what you’ve built. At Ramp, everyone is a builder who owns problems end to end and makes consequential decisions that shape the outcome. The median Ramp customer saves 5% and grows revenue 16% in their first year – far in excess of businesses operating without Ramp. We believe every ambitious company deserves the same. If you want to build systems that directly shape how companies move and manage billions, Ramp is the place to do it. About the Role We’re on a quest for our next social media expert. Not a guru, not a ninja, but a seasoned professional who can produce product launches, write clever skits, sling SaaS memes, and craft thoughtful messaging. If you consider yourself Terminally Online™ with a habit for trend spotting and making heads tilt on the legal team, you're our type. We’re not after a hands-off strategist. We’re after a hands-on operator with a track record for thinking strategically and acting creatively. Ramp sits at the intersection of finance and tech. Our goal is to bring it into the mainstream. We need someone who's curious about these worlds and can expand our brand beyond them. It’s not enough to “go viral.” We’re here to become a staple in every company on the planet. You'll be a key builder in making that happen. This role will report to our Social Media Lead under the Head of Communications. What You'll Do Company Brand Co-own all social channels - LinkedIn, X, Instagram, YouTube, and Facebook with content, community management, and calendar planning.

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars. SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next. ABOUT THE ROLE: We’re seeking exceptional Applied engineers to join a high-priority project that approximately 600 million monthly users use. This is an exciting opportunity for individuals with a engineer or scientist background to apply their skills to recommendation systems, ranking algorithms, search technologies, and many other systems. You’ll work at the intersection of advanced AI development and real-world impact, enhancing the ability to connect users with relevant content, accounts, and experiences. RESPONSIBILITIES: Designing and architecting recommendation algorithms across various product surfaces Leverage all of SpaceXSI's infra and AI stacks to dramatically enhance the user experience Write data pipelines and training jobs that continuously learn from product data. Iterate and improve the algorithm by gathering user feedback in real time through experimentation Ensuring scalability and efficiency of machine learning systems BASIC QUALIFICATIONS: Knowledge of data infrastructure like Kafka, Clickhouse, and Spark Experienced in implementing recommender systems and/or deep learning applications at industrial scale Skilled in one or more DL software frameworks such as

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions. You will leverage your skills to build ML and optimization models and productionalize pipelines that can scale to millions of calls per day while solving critical business problems that have a big impact on the marketplace and rider experience. You will get exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference and collaborate closely with teammates and stakeholders across Pricing, from Product Managers to Engineers and Analysts. We are looking for someone who is excited about working in a fast-paced, innovative, and impactful environment, and is adept at balancing complexity and efficiency to translate real world business problems into reliable solutions, systems and decision frameworks. Responsibilities Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context Write production quality code. Design, build and deploy production-grade ML and Optimization models. Able to build custom methods and tooling beyond off-the-shelf libraries. Perform data analysis and build proof-of-concepts to explore and propose ML and Optimization solutions to both new and existing problems. Evaluate machine learning systems against business goals. Collaborate with Engineers to implement algorithms in live systems and ensure the robustness of the systems Establish metrics and development measurement methodologies to monitor the health of our products, as well as the impacts on user and marketplace outcomes Drive collaboration and coordination with cross-functional teams

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The Pricing team is a centerpiece of Lyft’s marketplace, determining prices for all rideshare products and supporting new initiatives. Dynamic Pricing & Offer Selection sits at the heart of Pricing, focused on determining optimal prices and ETAs in real-time and balancing supply and demand for our two-sided marketplace to drive both short-term and long-term conversion and retention. As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions. You will leverage your skills to build ML and optimization models and productionalize pipelines that can scale to millions of calls per day while solving critical business problems that have a big impact on the marketplace and rider experience. You will get exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference and collaborate closely with teammates and stakeholders across Pricing, from Product Managers to Engineers and Analysts. We are looking for someone who is excited about working in a fast-paced, innovative, and impactful environment, and is adept at balancing complexity and efficiency to translate real world business problems into reliable solutions, systems and decision frameworks. Responsibilities Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context Write production quality code. Design, build and deploy production-grade ML and Optimization models. Able to build custom methods and tooling beyond off-the-shelf libraries. Perform data analysis and build proof-of-concepts to explore and propose ML and Optimization solutions to both new and existing problems.&n

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive. The Pricing team is a centerpiece of Lyft’s marketplace, determining prices for all rideshare products and supporting new initiatives. Dynamic Pricing & Offer Selection sits at the heart of Pricing, focused on determining optimal prices and ETAs in real-time and balancing supply and demand for our two-sided marketplace to drive both short-term and long-term conversion and retention. As an Applied Scientist specializing in Machine Learning and Operations Research on this team, you will develop mathematical models and launch algorithms that power these key pricing and ETA decisions. You will leverage your skills to build ML and optimization models and productionalize pipelines that can scale to millions of calls per day while solving critical business problems that have a big impact on the marketplace and rider experience. You will get exposure to a diverse set of real-world problems across optimization, prediction, machine learning, and inference and collaborate closely with teammates and stakeholders across Pricing, from Product Managers to Engineers and Analysts. We are looking for someone who is excited about working in a fast-paced, innovative, and impactful environment, and is adept at balancing complexity and efficiency to translate real world business problems into reliable solutions, systems and decision frameworks. Responsibilities Partner with Data Scientists, Engineers, Product Managers, and Business Partners to frame problems mathematically and within the business context Write production quality code. Design, build and deploy production-grade ML and Optimization models. Able to build custom methods and tooling beyond off-the-shelf libraries. Perform data analysis and build proof-of-concepts to explore and propose ML and Optimization solutions to both new and existing problems.&n

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

Most PMs write specs and wait for feedback. You'll be deploying AI agents with real customers across APAC, then turning what you learn into product direction that actually matters. The Company Sendbird is on a mission to build the AI workforce of tomorrow. For over a decade, we built the infrastructure behind conversations—chat, voice, video, messaging APIs—and became the #1 CPaaS platform for in-app communications. 4,000+ brands trust us. 7 billion messages flow through our platform every month. 300 million monthly active users. We powered conversations for DoorDash, Match Group, Noom, Yahoo Sports, Rakuten, and thousands of others. We were good at what we did. Really good. We also saw it early: AI would fundamentally reshape how businesses talk to customers. The infrastructure we'd spent a decade building would become commoditized. The value would move up the stack—into intelligence, into experience, into outcomes. We had a choice: protect what we built, or reinvent ourselves. We chose reinvention. In December 2024, we made the full strategic pivot to AI-first customer experience. By February 2025, we'd launched our AI agent for enterprise CX—built on a decade of conversation data, now with intelligence on top. And in November 2025, we rebranded to delight.ai. The name says it all. AI's real promise isn't efficiency or cost savings. It's giving customers back something they lost—the feeling of being truly understood and cared for. Not satisfied. Delighted. The Product Delight.ai is the AI concierge for customer experience. Most AI agents forget you the moment the conversation ends. Ours doesn't. Delight.ai builds memory over time, learns preferences, and connects context across every channel—chat, SMS, email, voice, WhatsApp—without losing the thread. We're building AI that makes customers feel understood, seen, and remembered. Why Forward Deployed Product Manager Enterprise AI isn't won in the roadmap, it's won in the field. Businesses across APAC are trying to f

Position Overview: As a Software Engineer II at Diligent, you’ll take on a hands-on technical role in building secure, scalable, and high-performing serverless microservices using TypeScript on AWS. You’ll contribute meaningfully to our mission of making governance effortless for our customers, working in a team of passionate and talented individuals that owns its services end to end—from architecture and implementation to monitoring and continuous improvements. This role is ideal for a mid-level engineer who writes solid code and embraces AI-powered tools to work smarter and faster. You’ll help shape architectural discussions, and scale modern development practices, including responsible use of AI in workflows. Key Responsibilities Design and implement secure, scalable, high-performing, yet simple solutions using AWS Serverless technology. These solutions should strive to be event-driven, highly observable, with infrastructure as code, and tightly leveraging AWS’s ecosystem of services. Optimize your development and delivery experience in order to maximize your team’s productivity and deploy continuously to production. Work in a collaborative environment where you regularly pair, plan, and execute tasks as a team and maintain a healthy development flow by adhering to Agile processes and driving iterative enhancements. Use AI tools to accelerate coding, debugging, testing, research, and code reviews, always validating outputs and applying judgment. Required Experience/Skills 3–5 years of professional software engineering experience in an agile, fast-paced environment. AI Tooling & Practices: Uses AI to boost productivity, skilled in prompt engineering, and evaluates AI outputs responsibly (bias, cost, ethics). Familiar with core AI concepts (tokens, context length, embeddings, hallucinations), understands high-level LLM behavior, and recognizes safe vs. unsafe use cases (privacy, security, fairness). Cloud & infrastructure basics: Hands-on with AWS ser

Senior Software Developers collaborate with Business and Quality Analysts, Designers, Project Managers and more to design software solutions that will create meaningful change for our clients. They listen thoughtfully to understand the context of a business problem and write clean and iterative code to deliver a powerful end result whilst consistently advocating for better engineering practices. By balancing strong opinions with a willingness to find the right answer, Senior Software Developers bring integrity to technology, ensuring all voices are heard. For a team to thrive, it needs collaboration and room for healthy, respectful debate. Senior Developers are the technologists who cultivate this environment while driving teams toward delivering on an aspirational tech vision and acting as mentors for more junior-level consultants. You will leverage deep technical knowledge to solve complex business problems and proactively assess your team’s health, code quality and nonfunctional requirements. Due to the nature of the projects and specific client security clearance, this role requires the successful candidate to be a Singapore Citizen or PR of Singapore. Job responsibilities You will learn and adopt best practices like writing clean and reusable code using TDD, pair programming and design patterns You will use and advocate for continuous delivery practices to deliver high-quality software as well as value to end customers as early as possible You will work in collaborative, value-driven teams to build innovative customer experiences for our clients You will create large-scale distributed systems out of microservices You will collaborate with a variety of teammates to build features, design concepts and interactive prototypes and ensure best practices and UX specifications are embedded along the way. You will apply the latest technology thinking from our Technology Radar to solve client problems You will efficiently utilize DevSecOps tools and practices to build an

Senior Software Developers collaborate with Business and Quality Analysts, Designers, Project Managers and more to design software solutions that will create meaningful change for our clients. They listen thoughtfully to understand the context of a business problem and write clean and iterative code to deliver a powerful end result whilst consistently advocating for better engineering practices. By balancing strong opinions with a willingness to find the right answer, Senior Software Developers bring integrity to technology, ensuring all voices are heard. For a team to thrive, it needs collaboration and room for healthy, respectful debate. Senior Developers are the technologists who cultivate this environment while driving teams toward delivering on an aspirational tech vision and acting as mentors for more junior-level consultants. You will leverage deep technical knowledge to solve complex business problems and proactively assess your team’s health, code quality and nonfunctional requirements. Job responsibilities You will learn and adopt best practices like writing clean and reusable code using TDD, pair programming and design patterns You will use and advocate for continuous delivery practices to deliver high-quality software as well as value to end customers as early as possible You will work in collaborative, value-driven teams to build innovative customer experiences for our clients You will create large-scale distributed systems out of microservices You will collaborate with a variety of teammates to build features, design concepts and interactive prototypes and ensure best practices and UX specifications are embedded along the way. You will apply the latest technology thinking from our Technology Radar to solve client problems You will efficiently utilize DevSecOps tools and practices to build and deploy software, advocating devops culture and shifting security left in development You will oversee or take part in the entire cycle of software consulting and