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. — 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: • Supervised fine-tuning and preference optimisation (DPO, RLHF, RLAIF) of open-weight language models, including building your own datasets via synthetic generation, noisy or weak supervision, and rejection sampling. • Shaping conversational behaviour over multiple turns: resistance to persuasion and sycophancy, calibrated confidence, and knowing when to accept corrections. • Alignment-style fine-tuning that changes a specific behaviour while preserving general capability. — Multilingual Pre-training — Experience with: • Training multilingual or low-resource-language models from scratch. • Tokenizer design across scripts and typologically diverse languages. • Balancing highly unequal per-language data (sampling temperatures, cross-lingual transfer) in data-constrained regimes. — Additional Areas of Interest — Experience in any of the following is a plus: • Scaling laws and training-efficiency research. • Curriculum learning and data ordering. • Model evaluation: benchmark construction, contamination control, statistically sound comparisons. • Uncertainty estimation and model calibration. • Data augmentation and synthetic data for robustness. — General Qualifications • 3+ years of machine learning research experience (PhD research counts toward this requirement). • Strong experience with PyTorch, JAX, TensorFlow, or similar ML frameworks. • Degree from a top-100 university, experience at a FAANG or comparable AI company, or an equivalent research track record through publications or impactful open-source contributions. — Why Join • Work on cutting-edge machine learning research. • Collaborate with leading AI researchers on challenging, high-impact projects. • Flexible, project-based work with competitive compensation.