LLM Research Scientist (Pre-training & Computer Vision & Adversarial Robustness)

We're looking for experienced machine learning researchers with hands-on experience training and improving deep learning models end-to-end, across vision and language. You'll work on well-scoped empirical open-ended ML research problems.

• Train image classifiers and generative image models from scratch, and fine-tune open-weight language models.
• Get the most out of limited data, compute, and model-size budgets.
• Make models robust — to adversarial inputs and to adversarial conversations.
• Compress models to meet hard size and latency constraints without sacrificing accuracy.
• Diagnose and resolve training issues.

We are looking for candidates with strong expertise in one or more of the following areas:

• Adversarial training of image classifiers (e.g. PGD-based training, TRADES).
• Evaluating robust accuracy under standard threat models (e.g. L∞ attacks, AutoAttack) and avoiding gradient-masking pitfalls.
• Managing the robustness–accuracy trade-off and robust overfitting.