About the role
About Traversal Traversal is the AI Site Reliability Engineer (SRE) for the enterprise—already trusted by some of the largest companies in the world to troubleshoot, remediate, and even prevent the most complex production incidents. Our mission is to free engineers from endless firefighting and enable them to focus on creative, high-impact work. Our roots remain deeply embedded in AI research, and we’re channeling that scientific rigor and creativity into building the premier AI agent lab for the enterprise. Hence, what we’re proudest of is assembling the most talented yet nicest group of individuals, including researchers from MIT, Harvard, and Berkeley, to world-class engineers from industry: Citadel Securities, Cockroach Labs, Datadog, DE Shaw, ServiceNow, Glean, Perplexity, Pinecone, and more, to take on one of the hardest problems for AI to solve. Without the entire team, none of this would be possible.
The Role As an AI Researcher at Traversal, you’ll work on training intelligent AI agents to perform root cause analysis (RCA) in complex, high-pressure production environments—even when labeled data is limited or nonexistent. Your work will shape the foundational reasoning capabilities of our agentic systems, powering insights that help engineers detect, diagnose, and resolve incidents across massive volumes of observability data. You’ll lead research across areas like LLM fine-tuning, reward modeling, synthetic data generation, and deep reinforcement learning, all in service of building more robust, interpretable, and autonomous agents. This is a high-impact role at the intersection of AI, infrastructure, and systems reliability, ideal for those with deep research experience looking to translate bold ideas into real-world impact.
Responsibilities LLM & Agent Research: Prototype and evaluate novel prompting strategies, reasoning workflows, and tool-use policies for agents handling large-scale observability data and complex troubleshooting workflows. Training & Alignment: Explore and apply fine-tuning, reinforcement learning, and reward modeling techniques to align AI behavior with real-world SRE workflows and debugging practices. Synthetic Data & Experimentation: Design pipelines to generate synthetic incidents and observability signals, enabling scalable training and testing in data-scarce scenarios. Cross-Team Collaboration: Work closely with AI engineers, infrastructure teams, and product leads to bring research into production and close the loop between experimentation and impact. Stay on the Frontier: Track the latest developments in LLMs, agentic architectures, and AI alignment—translating insights into actionable improvements.