AI Agent Platform Engineering Lead

Jobtailor; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Jobtailor in San Francisco seeks an experienced software engineer to lead the agent platform, architecting agent-driven workflows and internal systems in collaboration with researchers and product teams.

You will drive evaluation, observability, and feedback loops to improve agent reliability, performance, and learning, and establish scalable deployment patterns for safe production use.

Qualifications

• 3+ years building software at a frontier AI lab, agentic startup, leading hedge fund, big tech company, or similar.
• Strong familiarity with the modern AI stack (LLMs, tool use, orchestration, evaluation).
• Proficiency in Python and SQL; bonus for TypeScript, Go, or Rust.
• Basic understanding of financial markets and an interest in investing.
• Demonstrated experience designing, building, and deploying multi‑agent systems in production environments; expertise in agent orchestration, tool calling, context window management, and agent grounding.
• Proven ability to anticipate and address failure modes, with hands‑on experience in observability and evaluation of agent systems.
• BS/MS/PhD in Computer Science or related field, or equivalent practical experience.

Responsibilities

• Provide technical leadership across the agent platform
• Design and build agent-driven workflows and internal systems
• Build evaluation, observability, and feedback systems that improve agent reliability, performance, and learning over time
• Establish patterns and infrastructure for reliable agent deployment

Skills

Python

SQL

TypeScript

Go

Rust

AI stack

Agent systems

Observability

System design

Education

CS degree or equivalent

Job description

Jobtailor in San Francisco seeks an experienced software engineer to lead the agent platform, architecting agent-driven workflows and internal systems in collaboration with researchers and product teams.

You will drive evaluation, observability, and feedback loops to improve agent reliability, performance, and learning, and establish scalable deployment patterns for safe production use.