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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer - ML Observability - **Company:** Datadog - **Location:** Boston, MA, United States (Remote available) - **Experience:** Experienced - **Salary:** $234,000.0 - $300,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Software Debugging, Distributed Systems, Machine Learning, Software Engineering, Datadog, Delivery Pipeline, Large Language Models, Prompt Engineering, Model Validation, Generative AI, Backend, Information Technology - **Published:** September 29, 2026 - **Apply:** https://www.themuse.com/jobs/datadog/staff-software-engineer-ml-observability ## About the Role * You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience * Deep understanding of distributed systems and scalable backend architectures * Hands-on experience building and shipping LLM-powered or GenAI applications. * Understanding of model internals, inference pipelines, evaluation techniques, and prompt engineering * Ability to thrive in ambiguous, fast-changing spaces and have a product-oriented mindset * You're excited to shape the next generation of AI observability tools from the ground up * Communicate clearly, think rigorously, and take pride in clean, maintainable code * Experience with observability tools/platforms Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you're passionate about technology and want to grow your skills, we encourage you to apply. ## Description The ML Observability team builds cutting-edge tools to monitor, explain, and improve AI systems in production, particularly those leveraging Large Language Models (LLMs) and generative AI. We provide robust, scalable observability for AI workloads, including drift detection and model evaluation, and behavior tracing, enabling customers to ship AI with confidence. As a Staff Engineer, you'll lead the development of new features and foundational capabilities within Datadog's LLM Observability product. You will shape product direction, drive experimentation, and apply your deep understanding of both AI systems and software engineering to solve open-ended problems in the fast-moving AI landscape. Your work will directly impact how our customers monitor, troubleshoot, and optimize LLM-based applications in production. Join us in building the foundational tools that make AI systems observable, understandable, and reliable in the real world. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What You'll Do: * Drive design and implementation of LLM observability features. * Ideate, prototype, and scale new product features to provide insights and drive improvements for generative AI systems * Work cross-functionally with other eng teams, product, UX, and applied science to iterate fast and find product-market fit * Develop and extend tools for tracing, evaluating, and debugging LLMs * Influence architecture decisions and mentor engineers to build resilient, high-performance systems * Stay close to customer pain points and use those insights to guide product and engineering priorities * Stay current with industry trends and advancements in machine learning and observability, driving innovation within the team ## Related Videos - [Why LLMs Need Observability and How to Do It](https://www.wearedevelopers.com/videos/2117-why-llms-need-observability-and-how-to-do-it) - [Your imaginations is (no longer) the limit: how Generative AI empowers people to be creative](https://www.wearedevelopers.com/videos/741-your-imaginations-is-no-longer-the-limit-how-generative-ai-empowers-people-to-be-creative) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [The Memory Leak That Ate Our Cluster: A Postmortem](https://www.wearedevelopers.com/videos/2057-the-memory-leak-that-ate-our-cluster-a-postmortem) - [Mastering AI-Driven Problem Solving in Engineering with Observability](https://www.wearedevelopers.com/videos/994-mastering-ai-driven-problem-solving-in-engineering-with-observability) - [The shadows that follow the AI generative models](https://www.wearedevelopers.com/videos/624-the-shadows-that-follow-the-ai-generative-models) ## Related Articles - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 150 - The shift to AI generated code, fingerprinting and OKRs vs. doing your job](https://www.wearedevelopers.com/magazine/533-dev-digest-150-the-shift-to-ai-generated-code-fingerprinting-and-okrs-vs-doing-your-job) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)