LLM Research Scientist, Computer Vision and Robustness

Advance empirical machine learning research across computer vision and language modeling. This role focuses on training, improving, evaluating, and deploying deep learning systems within real data, compute, model-size, and latency constraints.

• Work on well-scoped, open-ended empirical machine learning research problems.
• Train image classifiers and generative image models from scratch, and fine-tune open-weight language models.
• Improve model performance when data, compute, and model-size budgets are limited.
• Strengthen models against adversarial inputs and adversarial conversations.
• Compress models to satisfy strict size and latency requirements while maintaining accuracy.
• Diagnose and resolve model-training issues.

Bring strong expertise in one or more of the following areas:

• Adversarial robustness: adversarial training for image classifiers, including PGD-based training and TRADES; robust-accuracy evaluation under standard threat models such as L∞ attacks and AutoAttack; avoiding gradient-masking pitfalls; and managing robustness-accuracy trade-offs and robust overfitting.
• Efficient computer vision: end-to-end image-classifier training, particularly fine-grained recognition with many visually similar classes and few examples per class; quantization, pruning, and knowledge distillation; and deployment under hard on-device, edge, or embedded size and latency budgets.
• Generative image modeling: training diffusion models, GANs, VAEs, or flow-based models from scratch; iterating against sample-quality measures such as FID; and applying training-efficiency techniques to build strong generators quickly with small parameter counts.
• LLM post-training and behavioral robustness: supervised fine-tuning and preference optimization, including DPO, RLHF, or RLAIF, for open-weight language models; creating datasets through synthetic generation, noisy or weak supervision, and rejection sampling; shaping multi-turn conversational behavior, including resistance to persuasion and sycophancy, calibrated confidence, and appropriate acceptance of corrections; and targeted alignment-style fine-tuning that preserves general capabilities.
• Multilingual pre-training: training multilingual or low-resource-language models from scratch; tokenizer design across scripts and typologically diverse languages; and balancing highly unequal language-data volumes using approaches such as sampling temperatures and cross-lingual transfer in data-constrained settings.
• Additional relevant experience: scaling laws, training-efficiency research, curriculum learning, data ordering, benchmark construction, contamination control, statistically sound model comparisons, uncertainty estimation, model calibration, data augmentation, or synthetic data for robustness.

You should have 3+ years of machine learning research experience, with PhD research counting toward this requirement, and strong experience with PyTorch, JAX, TensorFlow, or a comparable ML framework. A degree from a top-100 university, experience at a FAANG or comparable AI company, or an equivalent research record through publications or impactful open-source contributions is required.

• Remote, hourly engagement.
• Flexible, project-based work.
• Collaborate with leading AI researchers on challenging, high-impact research projects.

$100 to $120 per hour.