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

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

Benefits offered by this job

Weekly payments

Job summary

Synthires is seeking an LLM Research Scientist (Pre-training & Post-Training) to contribute to cutting-edge AI research in a remote, hourly-contract role. The candidate will lead pre-training, post-training, data curation, and evaluation for large language models, and collaborate with researchers to improve alignment, efficiency, and performance.

Experience with RLHF, SFT, and scalable training pipelines is a plus; a PhD or equivalent research track is highly valued.

Qualifications

• 3+ years of machine learning research experience, PhD qualifies.
• Strong expertise in LLM post-training, RL for LLMs, model alignment, and evaluation.
• Experience with PyTorch, JAX, TensorFlow or similar frameworks; transformer basics understood.
• Deep understanding of transformers, LLMs, optimization and modern deep learning methods.
• Excellent analytical and scientific communication skills; remote work capability.

Responsibilities

• Train transformer-based language models from scratch and fine-tune open-weight foundations.
• Design pre-training and post-training pipelines for large language models.
• Create, curate and optimize large-scale training datasets from diverse sources.
• Develop data filtering, deduplication, quality classification, and curriculum strategies.
• Diagnose and resolve training failures, convergence issues and instabilities.
• Build and evaluate SFT, DPO/RLHF/RLAIF, and reward modeling pipelines.
• Design evaluation benchmarks and analyze model performance with rigorous experiments.
• Collaborate with AI researchers to improve reasoning, alignment, efficiency and performance.

Skills

3+ years of ML research

LLM Post-Training

Reinforcement Learning for LLMs

Model Alignment

LLM Evaluation

Transformer architectures

PyTorch/JAX/TensorFlow

Education

PhD in Computer Science / ML / AI / NLP

Top-100 university / FAANG background

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.