Text Annotation Engineer

MicroAGI, Inc.; München; Vor Ort; EUR 60.000 - 90.000; Vollzeit

Zusammenfassung

MicroAGI, Inc. is seeking a proactive engineer to build the text annotation stage for our physical AI data platform in Munich. You will design end-to-end annotation workflows, define what a correct caption is, and implement the human-in-the-loop review to ensure high-quality language data.

You will work with Python and vision-language models, scale the pipeline from a few hours of footage to thousands, and establish robust quality metrics while owning a stage that does not yet exist.

Qualifikationen

• Strong Python experience and hands-on work with vision-language models or LLMs.
• Experience building annotation or labeling workflows, including the human side of them.
• Ability to define what counts as a correct caption and then measure it.
• Comfortable owning a stage that does not exist yet.
• High agency and proactive work attitude.

Aufgaben

• Build the text annotation stage from nothing. Captions, task descriptions, step segmentation.
• Use vision-language models for the first pass and measure how often they are right.
• Design the human-in-the-loop review that corrects what the models get wrong.
• Write the guidelines annotators follow, then rewrite them when they turn out to be ambiguous.
• Build quality metrics for language output, which is harder to score than a bounding box.
• Scale the stage from a few hours of footage to thousands.

Kenntnisse

Python

Vision-Language Models

Annotation Pipelines

Metrics & Evaluation

Proactivity/High Agency

Jobbeschreibung

The next ten years of AI will not be won in software. They will be won in the physical world. In factories, hospitals, kitchens, fields, and homes. The companies that own that data will own the century.

microagi is building it. We are the data layer for physical AI.

Nobody owns this stage yet. It is yours to build. Every clip we collect needs language attached to it. What the person is doing, what the task is, where one step ends and the next begins. You will build that with vision-language models where they are good enough, and with people where they are not.

What You Will Do

• Build the text annotation stage from nothing. Captions, task descriptions, step segmentation.
• Use vision-language models for the first pass and measure how often they are right.
• Design the human-in-the-loop review that corrects what the models get wrong.
• Write the guidelines annotators follow, then rewrite them when they turn out to be ambiguous.
• Build quality metrics for language output, which is harder to score than a bounding box.
• Scale the stage from a few hours of footage to thousands.

Requirements

• Strong Python and hands-on work with vision-language models or LLMs.
• Experience building annotation or labeling workflows, including the human side of them.
• You can define what counts as a correct caption and then measure it.
• Comfortable owning a stage that does not exist yet.
• High agency - you don't wait to be told what to do.

Nice to Have

• Video understanding or temporal action segmentation.
• Fine-tuning or evaluating VLMs.
• Experience managing external annotation teams.