LLM Research Scientist (Remote | $100 –$120/hr)

Synthires; California; Remote; USD 138,000 - 165,000; Part time

Job summary

Synthires is seeking an LLM Research Scientist for remote, hourly engagement to advance state-of-the-art foundation models. You will work on pre-training and post-training, data curation, model evaluation, and optimization across challenging research problems.

You will design pipelines for pre-training, fine-tuning, SFT, and reward modeling, while collaborating with AI researchers to improve reasoning, efficiency, and alignment in a remote setting.

Qualifications

• 3+ years of machine learning research experience (PhD research qualifies).
• Strong expertise in LLM post-training, pre-training, evaluation, and alignment.
• Experience with RLHF, DPO, or related preference optimization methods.

Responsibilities

• Train transformer-based language models from scratch and fine-tune open-weight foundation models.
• Design and optimize pre-training and post-training pipelines for large language models.
• Construct, curate, and optimize large-scale training datasets from raw web data sources.
• Develop data filtering, deduplication, and curriculum learning strategies.
• Diagnose and resolve training instabilities and convergence issues.
• Build and evaluate SFT, RLHF/RLAIF, and reward modeling pipelines.
• Design evaluation benchmarks and analyze model performance.
• Collaborate with researchers to improve model reasoning and alignment.

Skills

Machine learning research

PyTorch

JAX

TensorFlow

Transformer architectures

Model evaluation

RLHF / RLAIF

Python

Research communication

Education

PhD in CS / ML / AI

Tools

PyTorch

JAX

TensorFlow

Job description

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

Location: Remote

Engagement Type: Hourly Contract

Compensation: $100-$120/hour

About the Opportunity

This opportunity is for experienced Machine Learning Researchers and LLM Research Scientists interested in contributing to advanced AI research and evaluation projects. The role focuses on training, fine-tuning, and improving large language models (LLMs), conducting empirical research, and advancing state-of-the-art foundation model capabilities.

You will work on challenging research problems spanning LLM pre-training, post-training, data curation, model evaluation, alignment, and optimization.

Responsibilities

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


• Design and optimize pre-training and post-training pipelines for large language models.


• Construct, curate, and optimize large-scale training datasets from raw web and other data sources.


• Develop data filtering, deduplication, quality classification, and curriculum learning strategies.


• Diagnose and resolve optimization failures, convergence issues, and training instabilities.


• Build and evaluate supervised fine-tuning (SFT), preference optimization (DPO, RLHF, RLAIF), and reward modeling pipelines.


• Design evaluation benchmarks and analyze model performance using rigorous experimental methodologies.


• Collaborate with AI researchers to improve model reasoning, alignment, efficiency, and overall performance.

Required Qualifications

• 3+ years of machine learning research experience (PhD research qualifies).


• Strong expertise in one or more of the following areas:


• LLM Post-Training


• Reinforcement Learning for LLMs


• Model Alignment and AI Safety


• LLM Evaluation and Benchmark Development


• Strong experience with PyTorch, JAX, TensorFlow, or similar machine learning frameworks.


• Deep understanding of transformer architectures, large language models, optimization techniques, and modern deep learning methodologies.


• Excellent analytical, research, and scientific communication skills.


• Ability to work independently in a remote research environment.

Preferred Qualifications

• PhD in Computer Science, Machine Learning, Artificial Intelligence, Natural Language Processing, or a related field.


• Degree from a Top-100 university, experience at a FAANG or leading AI company, or an equivalent research track record through publications or impactful open-source contributions.


• Experience with:


• Scaling Laws


• LLM Evaluation


• AI Alignment


• AI Safety Research


• Publications in leading AI conferences or significant open-source contributions.

Compensation

• Competitive compensation of $100-$120/hour.


• Weekly payments.