AI Researcher

Responsibilities

• Prototype and evaluate prompting strategies, reasoning workflows, and tool-use policies for agents operating on large-scale observability data and complex troubleshooting workflows.
• Ship improvements to production.
• Build and maintain eval harnesses that measure real accuracy improvements on actual customer incident types — not just benchmark scores.
• Own the loop from hypothesis to production measurement.
• 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 developments in LLMs, agent architectures, and AI alignment, translating insights into actionable improvements for Traversal’s domain.
• Apply fine-tuning, reinforcement learning, and reward modeling techniques to align AI behavior with real-world SRE workflows.
• Design pipelines to generate synthetic incidents and observability signals, enabling scalable training and testing in data-scarce environments.

Requirements

• PhD in Computer Science, Electrical Engineering, Statistics, or a related technical field; demonstrated depth in LLMs, agents, or applied machine learning.
• Strong applied AI expertise, including strong working knowledge of LLMs, transformers, reinforcement learning, or neural networks in agentic systems.
• Strong judgment in model evaluation and experimental iteration to improve product accuracy and behavior.
• Strong software engineering depth, with the ability to work effectively in a complex production codebase and ship production-quality code.
• Experience shipping AI or ML systems to production.
• Experience running rigorous experiments, interpreting results, and quickly translating learnings into product improvements.
• Startup or early-team experience, with comfort operating in ambiguous environments and building without mature infrastructure.

Nice to Have

• Experience in SRE, observability, or backend systems, especially when paired with strong AI/ML depth.
• Experience with RLHF, synthetic data pipelines, or LLM evaluation tooling.
• Contributions to open-source agent frameworks such as LangGraph, DSPy, or similar.
• Research experience in LLMs, agents, or reinforcement learning, including publications in venues such as NeurIPS, ICML, or ICLR; top-tier conference publications are a plus.

Compensation

We offer competitive compensation, startup equity, health insurance, and additional benefits. The U.S. base salary range for this full-time, in-person role in New York is $160,000–$300,000, plus equity and benefits. Our salary ranges are based on location, level, and role. Individual compensation is determined by experience, skills, and job-related knowledge.

Why You Should Join Us

We’ll make sure you’re fully supported with health insurance, a great tech setup, flexible time off, and plenty of in-office snacks. We offer competitive salary and equity packages, and take thoughtful consideration with every hire on our small, high-impact team.

Traversal is fully in-office, 5 days a week, based in New York near Madison Square Park. We have a collaborative, hard-working culture and are energized by building the future of AI-powered software maintenance. Working here means owning meaningful parts of the product, having the flexibility to move fast, and learning constantly. This is a place to grow your career, make a real impact, and help define a new category of infrastructure software.