Staff Data Platform Engineer (Hybrid)
About the role
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.
The Mission
Our Staff Data Platform Engineers make a real impact on the safety and ROI of large language models and agentic applications across different verticals and domains. You will work on the cutting edge of envisioning and building new types of tools and algorithms to monitor, explain, and improve such applications and in turn empower our customers.
About The Team
Our engineering team is a dynamic group of builders and thinkers dedicated to solving some of the most cutting-edge challenges in AI safety and reliability. Working on exciting and an expansive range of topics, from the responsible deployment of machine learning models, large language models (LLMs), to complex agentic applications. Our projects are inherently cross-disciplinary, requiring expertise in systems engineering, product engineering, and data science to build robust, scalable solutions. We thrive in a collaborative environment where continuous learning is at the forefront, ensuring every team member stays on their toes with the latest advancements in AI.
What You’ll Do
- Design and build core services and components of a world-class cloud platform to help enterprises develop, monitor and improve their full suite of AI based applications (covering predictive models, LLMs, GenAI models and agentic applications)
- Lead the design and implementation of distributed systems and microservices that compute, persist, and expose new ML + agentic observability metrics (e.g., response relevancy, hallucination scores) from raw trace data
- Design enterprise-grade, scalable data infrastructure, services and APIs to support enterprise scale work