Full-Time

Agent Engineer / Forward Deployed Engineer

Intent Lab

Intent Lab

1-10 employees

Autonomous AI software engineering platform

No salary listed

Sunnyvale, CA, USA

In Person

Onsite role; candidates must be based in the Bay Area or willing to relocate.

Category
Software Engineering (1)
Required Skills
LLM
Microsoft Azure
Python
Distributed Systems
Docker
TypeScript
AWS
Go
DevOps
Google Cloud Platform

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Requirements
  • At least 3 years of software engineering experience with a strong foundation in backend or infrastructure work.
  • Hands-on experience with large language models and agent systems, including prompt engineering, tool use, evaluations, and multi-step orchestration.
  • Familiarity with distributed systems, containers, continuous integration and continuous delivery, and cloud platforms such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure.
  • Proficiency in at least one modern programming language, such as Python, TypeScript, or Go.
  • Strong written and verbal communication skills, including comfort working with senior engineers and executives.
  • Ability to work independently in ambiguous environments, handle context switching, and sustain work in high-intensity conditions.
  • Based in the Bay Area or willing to relocate for an onsite role.
Responsibilities
  • Lead end-to-end technical deployments of IntentLab inside customer engineering organizations.
  • Integrate the system into customer codebases, continuous integration and continuous delivery pipelines, and cloud infrastructure.
  • Own the customer technical lifecycle from first integration through steady-state operation.
  • Build and tune custom agent behaviors, tool integrations, and evaluation harnesses for each customer's backend.
  • Debug agent performance across distributed systems and unfamiliar production code.
  • Validate integrations and maintain reliable system performance in customer environments.
  • Lead demos, pilots, and technical workshops with engineers, platform leads, and chief technology officers.
  • Build relationships with customer engineering teams and drive adoption from within those teams.
  • Translate customer pain and usage patterns into product and research priorities.
  • Ship fixes and features directly rather than only filing tickets.
  • Write documentation, reference integrations, and playbooks that turn one-off wins into repeatable implementations.
  • Identify problems, propose solutions, and drive them through completion.

Intent Lab builds Fleet, an autonomous AI system that acts as an engineering team to turn high-level intents into production-grade software, handling architecture, coding, deployment, testing, and ongoing optimization. Fleet takes natural-language requests and creates software tailored to a user’s constraints and workflow, instead of just generating code. It differentiates itself by delivering end-to-end lifecycle management with built-in quality and performance checks, resulting in production-ready systems. The company aims to shift how software is created by generating and refining software directly from user intent, producing fully verified solutions customized to each task.

Company Size

1-10

Company Stage

N/A

Total Funding

N/A

Headquarters

San Francisco, California

Founded

2026

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Simplify Jobs

Simplify's Take

What believers are saying

  • Jia's July 28 launch post drew massive attention, signaling strong founder-led distribution.
  • July 2026 demos showed 6.3x GLM-5.2 speedup and 6-million-test database execution.
  • Ashby's July 15 product-manager posting shows Intent Lab is hiring for commercialization.

What critics are saying

  • Intent Lab disclosed no customers, pricing, or revenue, leaving July 2026 traction unproven.
  • OpenAI Codex, Cursor, and Anthropic already own developer mindshare and enterprise distribution.
  • Fleet must prove reliability against production outages, or enterprises will reject autonomous delivery.

What makes Intent Lab unique

  • Yangqing Jia launched Intent Lab on July 28, 2026, bringing Caffe and ONNX credibility.
  • Fleet targets full software delivery, not just code completion, spanning architecture through verification.
  • Early demos include GLM-5.2 inference, one-shot SQLite creation, and verified filesystem automation.

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