LLM Research Scientist (Pre-training & Post-Training) | $100-$120/hr Remote
Overview
We're looking for experienced machine learning researchers who have hands-on expertise in training and improving large language models from end to end. In this role, you'll tackle clearly defined, open-ended empirical research problems related to LLM pre-training and post-training, working remotely on a flexible hourly basis. It's an opportunity to push the boundaries of foundation model research while collaborating with top AI researchers.
What You'll Do5
- 1Design and train transformer-based language models from scratch, and fine-tune existing open-weight models for specific tasks.
- 2Maximize model performance under constraints on data size and computational budget, efficiently allocating resources across model dimensions.
- 3Build high-quality training corpora by processing raw web-scale data sources, including filtering, deduplication, and quality classification.
- 4Develop post-training pipelines that include supervised fine-tuning, preference optimization, and alignment to domain-specific requirements.
- 5Diagnose and resolve training issues such as optimization failures, convergence problems, and instabilities during model training.
Requirements4
- 1At least 3 years of machine learning research experience (PhD research counts), with a strong focus on training language models.
- 2Proficiency in PyTorch, JAX, TensorFlow, or similar deep learning frameworks, plus hands-on work with transformer architectures.
- 3Demonstrated expertise in one or more of: pre-training data construction, foundation model pre-training, or LLM post-training (SFT, RLHF, DPO).
- 4A degree from a top-100 university, experience at a FAANG or comparable AI company, or equivalent research output via publications or impactful open-source contributions.
Who Should Apply
You're a hands-on researcher who thrives on empirical challenges and has a deep understanding of transformer architectures and language model training dynamics. You enjoy working independently on well-scoped problems and have a track record of improving model performance through data curation, training efficiency, or alignment techniques. If you're excited about advancing the state of the art in LLM research while working on flexible, project-based engagements, this role is for you.
Salary Insight
The compensation is $100.00 - $120.00 per hour, based on experience and project scope.
Application Tip
When applying, include a brief summary of a specific LLM pre-training or post-training project you led—especially one where you optimized performance under limited data or compute. Linking to a published paper, a GitHub repository, or a blog post that details your approach will set you apart.
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