ML Researcher, Representation Learning

Dunia Innovations GmbH
Berlin, Germany
23 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Languages
English
Job source

Tech stack

Artificial Intelligence Experimental Data Machine Learning Software Engineering Deep Learning Information Technology

Job description

Berlin, Berlin, 12489 Vollzeit 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 which don’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

Requirements

  • 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 recordof 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 ofresearch 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

About the company

Dunia, meaning “world” in over 20 languages, reflects our focus on building technologies that deliver abundance globally. By combining physics, AI, and automation, we accelerate materials discovery for next-generation energy and industrial systems. Our work helps make energy more accessible and materials more affordable and resilient while reshaping how science moves from idea to impact. Join us to work on problems where progress truly compounds.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.adzuna.de

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:23 min

Exploring specialized career paths within the data science ecosystem

Julian Joseph · LIVE

1:25 min

Distinguishing artificial intelligence from deep learning

Sam Witteveen · Coffee With Developers

2:36 min

Applying supervised machine learning for practical rule extraction

Katja Träumner

1:42 min

Balancing security compliance regulations with rapid AI experimentation

Damandeep Kochhar Damandeep Kochhar +4 · WWC Europe 2026

3:51 min

Overcoming hardware configuration barriers in machine learning

Jose Luis Latorre Millas · LIVE

2:03 min

Solving complex engineering challenges in artificial intelligence deployment

Nico Axtmann · WWC 2022

Videos

See all

Related articles

See all