Senior AI Scientist, Materials Discovery (Founding Team)

Thermia is the first AI-native solution vertically built for the industrial thermal chain, discovering breakthrough fluids and co-optimizing associated device, from Molecules to industrial systems.

We are a team of machine learning scientists, computational chemists, and thermal engineers united by a single ambition: to create a fundamentally new way of designing the fluids and systems that move heat through our modern economy.

Today's approach to thermal fluid and system design is fragmented, slow, and structurally incompatible with the urgency of the energy transition. Chemists develop fluids without knowledge of the systems they will run in. Engineers design thermal hardware without optimizing the working fluid. The result is decades of R&D, billions in cost and solutions that are sub-optimal by construction.

Thermia replaces this fragmented process with an integrated, physics-grounded discovery pipeline: AI accelerates what human expertise makes possible, and experiments validate what AI generates. Our platform generates entirely novel molecular candidates and co-optimizes the thermal systems associated with each discovery, building a proprietary portfolio of qualified molecules, fine-tuned models, and experimental data with every engagement.

Our work is powering the thermal infrastructure the energy transition cannot wait for.

Place: Paris, France. Level: Senior, Contract: Permanent, Mode: Hybrid

Snapshot We're looking for an experienced research scientist to design and train the foundation models behind Thermia's thermal materials discovery platform, as part of our founding team.

The Role As a Senior Research Scientist, you will use deep learning and chemistry know-how to design and train models that predict properties and generate molecular candidates for next-generation thermal fluids. You will implement code, run large-scale experiments, own results end-to-end, communicate them internally and externally, and help shape the technical direction of the AI team.

Your work may involve:

• Designing and training models for molecular property prediction and generative design (GNNs, equivariant networks, diffusion, flow matching)

• Implementing algorithm ideas and running end-to-end experiments (setup, training, analysis, iteration)

• Building and scaling training pipelines, datasets, and evaluation benchmarks

• Designing evaluations and ablations that answer real questions about model quality and scientific value

• Collaborating closely with computational chemists and thermal engineers to ground models in physical reality

• Sharing skills, reviewing work, and helping junior scientists grow

• Communicating results clearly through plots, writeups, and paper-ready narratives

About You

• 5+ years of experience applying AI to materials, molecules, or drug discovery in industry

• A research track record in molecular ML, generative chemistry, or related, including peer-reviewed publications or shipped systems

• Strong implementation ability and comfort working in research codebases (PyTorch or JAX)

• Evidence of owning experiments end-to-end, including analysis and interpretation

• Strong communication and a bias toward clarity and honesty regarding results

• High agency: you push projects forward, prioritize effectively, and take initiative

In addition, the following would be an advantage:

• Practical experience across the full pipeline: property prediction and generative modeling

• Strong experimental taste judgment on baselines, ablations, and what's worth testing

• A focus on craft: excellent work at high velocity

We highly encourage you to apply even if you do not meet all requirements. We take a holistic view of people's backgrounds and do not expect expertise in every area. We do expect you to learn quickly, take ownership, and bring others up with you.