Harness Engineer (AI Agent Systems)

Qualifications

• 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

• Think in constraints, invariants, and feedback loops

• Care about correctness, not just output quality

• Have automated real workflows end-to-end

• Prefer building systems over features

• Have only built demos or prototypes

• Avoid debugging or failure handling

• Project (GitHub)\ An agent system that:

• 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?

• structured

• thinking in systems instead of code

• caring about correctness instead of speed

• debugging behavior instead of writing features

• 16 more items(s)

Benefits

• Strong systems thinking

Responsibilities

• Not demos.\ Agents that execute workflows end-to-end and produce correct outcomes

• build those agents

• and build the systems that make them reliable

• 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

• 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

• Mostly prompt models and accept outputs

• performs a multi-step task

• includes validation

• Agents complete real workflows with minimal human input

• Outputs are correct by construction

• Failures decrease over time

• reliable

• 16 more items(s)

More job highlights

Job description

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

• 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

• Prefer 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.

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