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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.