ML Researcher, Representation Learning

DUDE CHEM; Berlin; Hybrid; EUR 70.000 - 110.000; Vollzeit

Zusammenfassung

Dunia is seeking an ML Researcher, Representation Learning, to invent representations that let models reason about molecules, materials, and physical processes. You will push the boundaries from theory to real discovery loops used by scientists.

You will design how matter is encoded, build multi-modal foundation models, and test ideas in production-grade systems, collaborating with chemists, physicists, and engineers.

Qualifikationen

• PhD or equivalent training in ML or related field.
• Deep experience in representation learning and modern deep learning architectures.
• Track record of designing representations for structured or scientific data.
• Strong software engineering skills and production-ready systems.
• Fluent in English; additional language desirable.

Aufgaben

• Define how matter is encoded.
• Build multi-modal foundation models.
• Work where research meets reality, testing ideas in production-grade systems.
• Push representation learning forward by introducing probabilistic reasoning and uncertainty awareness.

Kenntnisse

Representation learning

Deep learning architectures

Software engineering

English fluency

Ausbildung

PhD or equivalent

Jobbeschreibung

Your mission

Invent the representations that make matter legible to machines Most AI systems succeed because the world they model is forgiving. Materials discovery is not.

At Dunia, we are building AI systems that must reason about molecules, materials, and physical processes across scales, regimes, and sparse data. The limiting factor is not model size. It is representation.

As ML Researcher, Representation Learning, you work on the hardest layer of AI for Materials: deciding what the machine should see. You invent representations that let models generalize, transfer, and reason under uncertainty, and you test those ideas inside real discovery loops that include experiments and simulations.

This role is for someone who wants their representation ideas to matter beyond papers.

Your tasks will include:

Define how matter is encoded

• Design representations for molecules, materials, and processes that respect physical and chemical structure


• Decide what information should be explicit, implicit, or learned


• Build abstractions that transfer across tasks and domains

Build multi-modal foundation models

• Combine graphs, sequences, text, images, simulations, and experimental data


• Explore how different modalities reinforce or contradict each other


• Move beyond benchmark metrics toward models that support real decisions

Work where research meets reality

• Test ideas in production-grade systems used by scientists


• Collaborate closely with chemists, physicists, and automation teams


• Learn quickly which ideas hold up and whichdon’t

Push representation learning forward

• Introduce probabilistic reasoning and uncertainty awareness


• Challenge existing paradigms when they break down in physical domains


• Help shape the broader direction of AI-native science at Dunia

Your profile

• PhD or equivalent training in machine learning, computer science, or a closely related quantitative field, with4–8 years of post-degree experiencein research-driven ML roles


• Deep experience inrepresentation learningand modern deep learning architectures


• Track recordof designing representations forstructured or scientific datathat generalize beyond a single task or dataset


• Strong software engineering skills, with experience turning research ideas intorobust, maintainable systems


• Comfortable working at the boundary ofresearch and production,theoryand engineering


• Curious about the physical world and motivated by applying ML to chemistry, physics, or materials


• Willing to challenge existing paradigms when they break down in real-world settings


• Fluent in English; additional language desirable