# Machine Learning Scientist (all genders) - Hybrid, Berlin at Dida

> Hybrid · Germany · Unspecified · ML / AI · open. Source: https://alderhq.app/jobs/personio%3Adida%3A2548260 (read 2026-11-08).

## Facts (observed)

- Company: [Dida](https://alderhq.app/companies/dida)
- Location text: Berlin
- Where it can be done from (inferred): Hybrid · Germany (evidence: "Location: Berlin")
- Posted: 2026-02-27 (254 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://dida.jobs.personio.de/job/2548260
- Skills listed (inferred): Python

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (Germany, n 5): p25 €68k, median €85k, p75 €175k

## Description (the employer's text)

### Your mission

You will

- support the dida team in applying cutting-edge machine learning algorithms
- experiment with model architectures to find the best solution for each specific project
- keep up to date with advances in machine learning research and actively take part in our company’s collaborative learning and development environment.
### Your Profile

- a MSc or PhD in mathematics or physics
- a creative mind that likes to solve problems
- interest in modern machine learning approaches (experience in deep learning is a bonus)
- solid programming experiences (Python is a bonus)
- CV (mandatory)
- (Uni) Degree(s) (mandatory)
- Transcripts (where applicable)
- Project / Code Examples / Portfolio (optional)
### Why us?

Here are short descriptions of some projects we have worked on:

### Estimate the amount of solar panels that fit on a roof (computer vision)

Given a satellite picture and a ground image of a house, automatically detect certain elements of a roof (including obstacles, dormers etc.) in order to find out how many solar panels fit on it. This involves inferring 3d information from 2d pictures in order to infer the roof pitch.

### Detect, classify and suggest legal effectiveness of text paragraphs (NLP)

Automatically go through thousands of legal documents with the goal to classify dedicated paragraphs and check their legal effectiveness. This involves converting scans to text, coming up with a labelling scheme (problem modeling), and detecting different paragraphs automatically, before tackling the inference task.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/personio%3Adida%3A2548260 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.