Machine Learning Scientist - Hybrid, Berlin
Berlin
- Pay
- Pay not listed
- Work setting
- Not stated
- Employment
Permanent — employment source
Employment type in the employer’s posting: Permanent employee
From the employer’s posting
What you’ll work on
Full postingApply cutting-edge machine learning algorithms to customer projects
Experiment with model architectures to find the best solution per project
Keep up to date with machine learning research advances
From the employer’s posting
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
What you’ll bring
All qualificationsCore experience
- MSc or PhD in mathematics or physics
- Creative mind that likes to solve problems
- Interest in modern machine learning approaches
- Solid programming experience
Preferred experience
- Experience in deep learning
- Python programming
Qualification wording
You have a MSc or PhD in mathematics or physics
a creative mind that likes to solve problems
interest in modern machine learning approaches
solid programming experiences
experience in deep learning is a bonus
Python is a bonus
Education & alternatives
You have - a MSc or PhD in mathematics or physics - a creative mind that likes to solve problems
Tools in this posting
- Python
Source — Tool mentions in context
- interest in modern machine learning approaches (experience in deep learning is a bonus) - solid programming experiences (Python is a bonus) Minimum type of documents to attach:- CV (mandatory)
Posting record
Report an errorOpen 4 monthsWe first saw this posting 149 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 2, 2026 · 149 days ago
- Recorded sightings
- 62
- Last seen by us
- Oct 30, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the pay range before committing time to the process.
Complete your application on dida.jobs.personio.de. The employer’s form will show what is required.
Already applied? Track this application
Job description
dida is a machine learning software company with exciting problems for instance in computer vision and natural language processing. Our team tackles applied problems for different customers by using latest scientific advancements (especially in deep learning) and therefore believes that research oriented thinking can help solving real-world problems more efficiently.
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
You have
- 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?
You will work with an interdisciplinary team of people with a solid background in mathematics and statistics. We offer flexible working hours (full and part time) and have a nice office with good coffee in Berlin Kreuzberg, close to Moritzplatz. We prefer a hybrid work model, but are open to remote work. We believe in science and support with publishing your research results.
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.
What we need from you:
Source details
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- Pay
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- Location & working pattern
Berlin
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