Staff Platform Engineer

Our Purpose At Fiddler, we understand the implications of AI and the impact that it has on human lives. Our company was born with the mission of building trust into AI. The rise of Generative AI and Agents has unlocked generalized intelligence but also widened the risk aperture and made it harder to ensure that AI applications are working well. Fiddler enables organizations to get ahead of these issues by helping deploy trustworthy, and transparent AI solutions. Fiddler partners with AI-first organizations to help build a long-term framework for responsible AI practices, which, in turn, builds trust with their user base. AI Engineers, Data Science, and business teams use Fiddler AI to monitor, evaluate, secure, analyze, and improve their AI solutions to drive better outcomes. Our platform enables engineering teams and business stakeholders alike to understand the "what", “why”, and "how" behind AI outcomes. Our Founders Fiddler AI is founded by Krishna Gade (engineering leader at Facebook, Pinterest, Twitter, and Microsoft) and Amit Paka (product leader at Microsoft, Samsung, Paypal and two-time founder). We are backed by Insight Partners, Lightspeed Venture Partners, and Lux Capital. Why Join Us Our team is motivated to help build trust into AI to enable society harness the power of AI. Joining us means you get to make an impact by ensuring that AI applications at production scale across industries have operational transparency and security. We are an early-stage startup and have a rapidly growing team of intelligent and empathetic doers, thinkers, creators, builders, and everyone in between. The AI and ML industry has a rapid pace of innovation and the learning opportunities here are monumental. This is your chance to be a trailblazer. Fiddler is recognized as a pioneer in the field of AI Observability and has received numerous accolades, including: 2022 a16z Data50 list, 2021 CB Insights AI 100 most promising startups, 2020 WEF Technology Pioneer, 2020 Forbes AI 50 most promising startups of 2020, and a 2019 Gartner Cool Vendor in Enterprise AI Governance and Ethical Response. By joining our brilliant (at least we think so) team, you will help pave the way in the AI Observability space. ABOUT THE TEAM Our Platform Engineering team is a group of builders who make hard things look easy. They design and maintain the systems that keep Fiddler running smoothly; quietly holding the universe together with code, caffeine, and collaboration. Spread across time zones but united in purpose, this crew thrives on solving real problems, sharing what they learn, and jumping in to help wherever they’re needed. They bring brains, grit, and a healthy sense of humor to everything they do. They’re the kind of teammates who fix the issue, explain how they did it, and still show up to celebrate the win with everyone else. Platform Engineering doesn’t just keep the lights on, we’re busy designing better ones. If you like working on problems that haven’t been solved before, you’ll thrive here. Fiddler’s backend is where GenAI observability becomes real, from designing authorization layers for enterprise-scale AI systems to pioneering MCP authorization patterns. The systems you build will directly determine how companies trust and monitor their AI in production. The Platform Engineering team owns the foundational data infrastructure that every Fiddler service depends on: event ingestion, streaming pipelines, time-series storage, aggregation, and the query layers that power monitoring and analytics. We operate at the intersection of streaming data, OLAP analytics, and multi-tenant SaaS infrastructure. The stack: Python, Kafka, ClickHouse, PostgreSQL, Redis, Celery, Kubernetes (AWS/GCP), with Ray for ML inference workloads. WHAT YOU'LL DO - Set technical direction for the data platform — Own the architecture roadmap for Fiddler's ingestion, storage, and query layers. Drive multi-quarter initiatives from problem framing through design, implementation, and rollout. - Design systems for 10x scale — Lead the evolution of our ClickHouse-backed analytics layer and Kafka-based ingestion pipeline to handle order-of-magnitude growth in event volume, query complexity, and tenant count. - Define the event model for next-generation AI workloads — Architect the data model and storage strategy for agentic application traces, LLM evaluation pipelines, and enrichment workflows — balancing flexibility, query performance, and schema evolution. - Drive cross-team technical decisions — Partner with the Backend, Monitoring, and Enrichment teams to ensure platform abstractions serve their needs. Represent the Platform perspective in company-wide architecture reviews. - Own platform reliability and cost efficiency — Establish SLOs, capacity planning processes, and cost optimization strategies for data infrastructure. Make build-vs-buy decisions for infrastructure components. - Raise the engineering bar — Mentor senior engineers. Establish patterns and…