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.
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