LLM Research Scientist (Pre-training & Post-Training)

We're looking for experienced machine learning researchers with hands-on experience training and improving language models end-to-end. You'll work on well-scoped empirical open-ended LLM research problems.

**Responsibilities**

• Train transformer-based language models from scratch and fine-tune open-weight models.

• Get the most out of limited data and compute.

• Construct training corpora from raw web-scale sources.

• Build post-training pipelines.

• Diagnose and resolve training issues.

**Requirements**

We are looking for candidates with strong expertise in one or more of the following areas:

Foundation Model Pre-training

Experience with:

• Training transformer-based language models from scratch, end-to-end.

• Data- and compute-constrained regimes: allocating a fixed budget across model size, tokens, and epochs.

• Diagnosing optimisation failures, convergence issues, and training instabilities.

Pre-training Data

Experience with:

• Corpus construction from raw web crawls and other large unfiltered sources.

• Data filtering, deduplication, quality classification, and mixture/ordering optimisation.

• Measuring data interventions rigorously.

LLM Post-Training

Hands-on experience with one or more of:

• Supervised fine-tuning, including building your own datasets via synthetic generation, noisy or weak supervision, and rejection sampling.

• Preference optimisation (DPO, RLHF, RLAIF) and reward modelling / human-preference prediction.

• Alignment fine-tuning: shaping refusal behaviour, truthfulness, and unbiased reasoning while preserving general capability.

• Fine-tuning for narrow, verifiable domains (math, code, games, structured prediction) where outputs can be checked programmatically.

Additional Areas of Interest

Experience in any of the following is a plus:

• Scaling laws and training-efficiency research.

• Curriculum learning and data ordering.

• LLM evaluation: benchmark construction, contamination control, statistically sound comparisons.

• Reinforcement learning for language models.

• Model alignment and AI safety.

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 foundation model research.

• Collaborate with leading AI researchers on challenging, high-impact projects.

• Flexible, project-based work with competitive compensation.