ML Scientist - 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 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, Auto Attack) 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 typo logically 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.

• 3+ years of machine learning research experience (PhD research counts toward this requirement).
• Strong experience with PyTorch, JAX, Tensor Flow, 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.

• Work on cutting‑edge machine learning research.
• Collaborate with leading AI researchers on challenging, high‑impact projects.
• Flexible, project‑based work with competitive compensation.