6 months ago
Base Salary
$175k - $290k/yr
Responsibilities
- Build and improve production agent systems across retrieval, tool use, document understanding, memory, and orchestration.
- Design evaluations and experiments to measure agent quality in production.
- Analyze traces, failures, and user behavior to drive product and architecture decisions.
- Work with operations, go-to-market, and deployed teams to understand workflows and agent breakdowns.
- Evaluate models, prompts, and system designs across real enterprise tasks.
- Own the cycle from ideation through implementation, measurement, and iteration.
Requirements
- Strong engineering fundamentals and the ability to ship production systems.
- Fluency in Python.
- Experience building or working on LLM-powered products, agent systems, or adjacent applied AI systems.
- An empirical approach using logs, traces, experiments, and real usage to investigate problems.
- Strong understanding of how retrieval, prompting, memory, tools, and user experience interact.
- High ownership and comfort working in ambiguity.
- Strong opinions about what makes agent systems effective.
- Experience with retrieval, search, or ranking systems is a plus.
- Experience designing evaluations, benchmarks, or feedback loops for LLM systems is a plus.
- Experience building internal tools, workflow products, or operator-facing systems is a plus.
- Experience in startups or other high-ownership environments is a plus.
Benefits
- Early-stage equity
- Catered lunch and dinners
- In-person work in New York, NY, five days per week
Tech Stack
Categories
About Auctor
Auctor builds an AI agent platform for professional services and software-implementation teams, automating requirements capture, documentation, discovery, and decision support to accelerate project delivery. It sells its product as enterprise SaaS to solution engineers, forward‑deployed engineers, and onboarding teams. Founded in 2025 and headquartered in New York, the privately held company is Series A–stage.