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.
About the role
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.
## Responsibilities
- 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.
## Requirements
We are looking for candidates with strong expertise in one or more of the following areas:
Adversarial Robustness
Experience with:
- 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.
Efficient Computer Vision
Experience with:
- Training image classifiers end-to-end, especially for fine-grained recognition (many visually similar classes, few examples per class).
- Model compression: quantization, pruning, and knowledge distillation from large teachers into small students.
- Deploying models under hard size or latency budgets (on-device, edge, or embedded settings).
Generative Image Modeling
Experience with:
- Training image generative models from scratch: diffusion models, GANs, VAEs, or flow-based models.
- Iterating against sample-quality metrics such as FID.
- Training-efficiency tricks that produce good generators quickly and at small parameter counts.
LLM Post-Training & Behavioral Robustness
Hands-on experience with one or more of: …
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