ML Researcher, Representation Learning

Pegel; Berlin; Híbrido; EUR 90.000 - 130.000; Jornada completa

Descripción de la vacante

Dunia seeks an ML Researcher focused on representation learning to design machine-readable representations for molecules, materials, and physical processes. You will craft representations that enable generalization, transfer, and uncertainty-aware reasoning, tested in discovery loops with experiments and simulations.

You will collaborate with chemists, physicists, and automation teams to move ideas from theory to production, shaping the direction of AI-native science at Dunia.

Formación

• PhD or equivalent training in ML/CS with 4-8 years of post-degree research experience.
• Deep experience in representation learning and modern deep learning architectures.
• Track record designing representations for structured or scientific data that generalize beyond a single task or dataset.
• Strong software engineering skills to turn research ideas into robust, maintainable systems.
• Willingness to challenge existing paradigms when they break down in real-world settings.
• Fluent in English; additional language desirable.

Responsabilidades

• Define how matter is encoded for ML models across molecules, materials, and processes.
• Build multi-modal foundation models combining graphs, sequences, text, images, simulations, and experimental data.
• Work at the interface of research and production with scientists to test ideas in real systems.
• Push representation learning forward by introducing probabilistic reasoning and uncertainty awareness.

Conocimientos

Representation learning

Deep learning architectures

Software engineering

Research to production

English fluency

Educación

PhD or equivalent training

Descripción del empleo

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 experience in research-driven ML roles
• Deep experience in representation learning and modern deep learning architectures
• Track record of designing representations for structured or scientific data that generalize beyond a single task or dataset
• Strong software engineering skills, with experience turning research ideas into robust, maintainable systems
• Comfortable working at the boundary of research and production, theory and 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