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
Role overview
Job description
The Technical Content team is looking for an experienced Content Optimization Specialist who will help grow the visibility of Datadog across traditional search and AI-assisted search platforms. As part of the Community organization, you will optimize written and video technical content for search engines, answer engines, generative engines, and AI/LLM experiences while identifying new opportunities as audience needs and search behavior continue to evolve. You should understand how content is surfaced, cited, and summarized across traditional search, AI-assisted search, and agentic platforms. The ideal candidate has hands-on experience optimizing content that targets search demand from a highly technical audience and wants to apply their skills to the evolving AEO/GEO landscape. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creati
…What they are looking for
Skills & requirements
Qualification
As part of the Community organization, you will optimize written and video technical content for search engines, answer engines, generative engines, and AI/LLM experiences while identifying new opportunities as audience needs and search behavior continue to evolve; You have 1-2 years of experience optimizing content for AI-assisted search and agentic platforms (ChatGPT, Claude, Gemini, Codex, etc.) You are proficient with several SEO/AEO tools, such as Profound, Conductor, Semrush, Ahrefs, and Google Search Console; You have 2-5 years of experience working as a technical content writer, journalist, or content strategist at an AI or SaaS company; We also consider qualified applicants regardless of criminal histories, consistent with legal requirements
Department · Community

Hiring company
Datadog
We tackle some of the hardest technical problems while delivering a product that "just works" for our customers. And we are backed by some of the best VCs in NYC and the world. Do you want to make a difference? Are you exceptional at your job, and intrinsically motivated by it? Do you eat hard problems for breakfast and find them beautifully simple solutions by lunchtime? Do you ever wish you were there in the early days of these startups everyone is talking about? If so, we want to hear from you.
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