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Traversal

AI Research Engineer

New York$180k–$300kfulltimemidAdded today

About this role

Traversal seeks an AI Research Engineer to build the infrastructure and systems that power their research team's work on AI agents for enterprise incident resolution. You'll design experiment harnesses, scale research prototypes to production, and partner closely with researchers to evaluate and ship improvements to LLM-based agents.

What you'll do

  • Build and maintain research infrastructure including experiment harnesses, evaluation pipelines, and debugging tooling
  • Scale research prototypes from one-off experiments to production-ready systems for enterprise customers
  • Design experiments with researchers, framing hypotheses and ensuring results are statistically reliable
  • Collaborate with AI engineers, infrastructure teams, and product to integrate research into production
  • Prototype and evaluate LLM prompting strategies, agent reasoning workflows, and tool-use policies
  • Stay current with LLM and agent architecture developments to inform improvements

What they're looking for

  • Backend development and scaled systems architecture
  • Python and FastAPI
  • AWS (ECS, Kubernetes, S3, Postgres)
  • Experimentation methodology and result evaluation
  • Observability and monitoring tools
  • LLM and agentic AI systems
  • Data pipeline and ETL work
  • API design and deployment

Benefits

  • Competitive base salary ($180k–$300k)
  • Startup equity
  • Health insurance
  • Opportunity to work on frontier AI agent research
  • Collaborative team with researchers from MIT, Harvard, Berkeley
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Traversal

Traversal builds an AI-driven site reliability platform that helps enterprises automate and optimize incident response. The company is hiring AI Adoption Engineers, Solutions Engineers, Forward Deployed Engineers, and Product Engineers to guide customers through deployment and adoption while developing core platform capabilities.

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Likely interview questions

  • Describe a time you ran an experiment to validate a hypothesis about system behavior—what was your process for designing it and trusting the results?
  • Walk us through how you've taken a prototype or research idea and scaled it to production; what were the bottlenecks?