Harness Engineer (AI Agent Systems)

What this role is

We build AI agents that do real work.

Not assistants. Not demos.

Agents that execute workflows end-to-end and produce correct outcomes.

Your job is to:

• build those agents
• and build the systems that make them reliable

This is not prompt engineering.

This is making AI work in production.

What you’ll do

You will:

• Build agents that execute multi-step workflows
• Design systems for validation, retry, and failure handling
• Define constraints (schemas, invariants, contracts)
• Add feedback loops (detect → debug → improve)
• Turn failures into reusable systems

What this role is NOT

• Not prompt engineering
• Not one-shot demos
• Not feature-heavy product work

You are building agents that do the work, and the systems that ensure they do it correctly.

Note: This is different from “vibe coding.” You won’t just prompt and accept outputs. You’ll build systems so results are reliable and repeatable.

What we’re looking for

• Strong systems thinking
• Background in:
• infrastructure, backend, or data systems
• developer tools or internal platforms
• Experience building reliable systems (not just features)
• Comfortable debugging complex, ambiguous problems

Important

• LLM experience alone is not enough.
• We care about how you make systems reliable.

Good fit if you

• Think in constraints, invariants, and feedback loops
• Care about correctness, not just output quality
• Have automated real workflows end-to-end
• Prefere building systems over features

Not a fit if you

• Mostly prompt models and accept outputs
• Have only built demos or prototypes
• Avoid debugging or failure handling

Application (required)

• Project (GitHub)

An agent system that:

• performs a multi-step task
• includes validation
• handles failures (retry, fallback, etc.)
• Short answer (5–10 sentences)

Describe a system where an AI agent failed.

What caused it, and how would you fix it?

How we measure success

• Agents complete real workflows with minimal human input
• Outputs are correct by construction
• Failures decrease over time
• New capabilities come from improving the system, not patching outputs

Why this matters

• AI models are already powerful.
• The bottleneck is making them:
• reliable
• structured
• production-ready
• The teams that win will not have better prompts.
• They will have systems where agents actually work.

Before you apply

• Most engineers won’t enjoy this role.
• It requires:
• thinking in systems instead of code
• caring about correctness instead of speed
• debugging behavior instead of writing features
• But if this clicks for you,
• you’ll be working on the actual frontier of software engineering.