LLM Research Scientist (Pre-training & Post-Training) (Train AI Models Part Time!)

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.