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, Meta, Hebbia, Perplexity, Glean, 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 Platform Engineer at Traversal, you’ll work on the core foundations that make Traversal’s AI possible while ensuring Traversal’s platform, products, and applications are delivered with a high bar for quality, reliability, resilience, cost-effectiveness, and maintainability. You will build and own the frameworks, shared libraries, application foundations, observability, and developer tooling that power Traversal’s systems and AI agents. This involves designing scalable distributed systems and abstractions, delivering core application framework components, implementing best practices for software architecture and software delivery while balancing velocity, research flexibility, and production reliability.
Responsibilities Lead the design and implementation of scalable, robust backend systems to support AI agents and observability tools. Design and implement high-performance APIs to enable seamless communication between backend systems and frontend interfaces. Architect scalable distributed systems to support real-time workloads over petabytes of heterogeneous telemetry data. Build live evaluation pipelines, automated scoring systems, and benchmarks to measure and drive AI performance. Collaborate with AI engineers and scientists to integrate AI-driven insights and solutions into backend systems. Monitor, optimize, and scale backend services to handle high volumes of data while ensuring low-latency performance.