Remote | LLM Training & Alignment Research Scientist — $95–$115/hour

We are sharing a specialised part-time consulting opportunity for experienced machine learning researchers with hands-on expertise in foundation model pre-training, large-scale data pipelines, language model post-training, and empirical LLM research.

This role focuses on well-scoped, open-ended research problems involving the end-to-end training and improvement of transformer-based language models. Selected researchers will train models from scratch, fine-tune open-weight systems, build pre-training corpora and post-training pipelines, diagnose training failures, and investigate methods for improving performance under limited data and compute budgets.

Key Responsibilities

Foundation Model Pre-Training

• Train transformer-based language models from scratch across full end-to-end workflows
• Design experiments involving model size, token allocation, training duration, and compute budgets
• Investigate performance in data- and compute-constrained regimes
• Diagnose optimisation failures, convergence issues, and training instabilities
• Evaluate interventions using rigorous empirical comparisons

Pre-Training Data Development

• Construct training corpora from raw web crawls and other large-scale unfiltered sources
• Develop pipelines for filtering, deduplication, quality classification, and data selection
• Optimise dataset mixtures, sequencing, and curriculum strategies
• Measure the impact of data interventions on downstream model behaviour
• Identify contamination, duplication, quality, and coverage issues within training datasets

LLM Post-Training & Alignment

• Build supervised fine-tuning pipelines using curated, synthetic, weakly supervised, or rejection-sampled datasets
• Conduct preference optimisation using methods such as DPO, RLHF, or RLAIF
• Develop reward models and systems for predicting human preferences
• Improve refusal behaviour, truthfulness, robustness, and unbiased reasoning while preserving general capability
• Fine-tune models for verifiable domains such as mathematics, code, games, structured prediction, or other programmatically evaluated tasks

Research Evaluation & Optimisation

• Design statistically sound experiments and benchmark comparisons
• Evaluate training efficiency, scaling behaviour, and generalisation
• Develop contamination controls and robust model-evaluation protocols
• Analyse model failures and propose targeted training or data interventions
• Document research findings, experimental methodology, and technical conclusions clearly

Ideal Profile

Strong candidates may have:

• At least 3 years of machine learning research experience, including qualifying doctoral research
• Hands-on experience training or fine-tuning transformer-based language models
• Strong expertise in one or more of foundation model pre-training, pre-training data, or LLM post-training
• Experience working with PyTorch, JAX, TensorFlow, or comparable machine learning frameworks
• Ability to design and execute empirical research independently
• Strong understanding of optimisation, evaluation methodology, and experimental design
• Excellent technical writing, analytical reasoning, and research communication skills
• Experience working with large-scale datasets and distributed training systems

Educational Background

• A degree in computer science, machine learning, artificial intelligence, mathematics, statistics, engineering, or a related discipline is highly relevant
• PhD research in machine learning, natural language processing, deep learning, or a related field may count towards the experience requirement
• A strong publication record, impactful open-source contributions, or comparable applied research experience may also be considered
• Research experience at a leading university, technology company, AI organisation, or research laboratory may strengthen an application

Nice to Have

• Research experience involving scaling laws or training efficiency
• Familiarity with curriculum learning, data ordering, and mixture optimisation
• Experience constructing LLM benchmarks and controlling for training-data contamination
• Background in reinforcement learning for language models
• Expertise in reward modelling, preference learning, or human-feedback pipelines
• Experience with model alignment, AI safety, truthfulness, or refusal behaviour
• Familiarity with synthetic data generation and weak-supervision methods
• Publications or significant open-source contributions related to foundation models or language-model training

Why This Opportunity

• Work on cutting-edge foundation model research
• Investigate challenging empirical problems across pre-training and post-training
• Apply advanced machine learning expertise to high-impact language-model development
• Collaborate asynchronously with experienced AI researchers
• Explore methods for improving model capability, efficiency, reliability, and alignment
• Participate in flexible project-based work with competitive hourly compensation

Contract Details

• Independent contractor role
• Fully remote with flexible scheduling
• Competitive rates between $95–$115 per hour depending on expertise and project scope
• Work may include model training, dataset development, post-training pipeline design, evaluation, and experimental research
• Weekly payments via Stripe or Wise
• Projects may be extended, shortened, or adjusted depending on scope and performance
• Work will not involve access to confidential or proprietary information from any employer, client, or institution

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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