Research Engineer (Materials Foundation Models)
The role is about training and judging large AI models that learn from crystal structures, material properties, text and images. A strong candidate has solid software engineering habits, hands-on experience with distributed training on many GPUs, and real depth in one area such as representation learning, language model post-training or reinforcement learning. Each person owns a model from data through to integration, so comfort with both experiments and production quality code matters.
How it fits the AI era
Here AI is the product itself: the models propose and rank candidate materials, and engineers decide which modelling choices actually hold up through scaling studies and careful evaluation. What stays human is judging whether results make scientific sense, designing good data and tests, and working with chemists and materials scientists to turn model output into real lab decisions.
How it builds AI experience
The work involves training multi-modal foundation models on terabytes of scientific data across hundreds of GPUs, plus fine-tuning and post-training language models for scientific tasks. Someone new to materials can build a head start by studying how crystal structures are represented for machine learning and by trying a small project that learns embeddings from structured scientific data or figures.
Worth showing
- Distributed multi-GPU training project
- Self-supervised or contrastive learning
- LLM fine-tuning or RL post-training
- Evaluation suite and data curation
- Learning chemistry vocabulary quickly
This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.
About CuspAI
An AI search engine for new materials that the company says turns a wanted set of physical properties into synthesizable chemical designs in months instead of years of lab trial and error.
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