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.

We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

Contract and Payment Terms

• You will be engaged as an independent contractor.
• This is a fully remote role that can be completed on your own schedule.
• Projects can be extended, shortened, or concluded early depending on needs and performance.
• Your work at Mercor will not involve access to confidential or proprietary information from any employer, client, or institution.
• Payments are weekly on Stripe or Wise based on services rendered.
• Please note: We are unable to support H1-B or STEM OPT candidates at this time.

About Mercor

Mercor partners with leading AI labs and enterprises to train frontier models using human expertise. You will work on projects that focus on training and enhancing AI systems. You will be paid competitively, collaborate with leading researchers, and help shape the next generation of AI systems in your area of expertise.