Quantitative LLM Researcher at Placeholder

Numerai is the hardest data science tournament in the world, powering an AI hedge fund. Every week we distribute and process terabytes of data, ingest millions of stock market predictions, execute thousands of Machine Learning models, and trade based on the results. Now, we are looking for a talented researcher to drive the development and application of LLMs in the world of quantitative finance.

Location: San Francisco

Compensation: $300k-$350k + incentives + usual benefits + flexible paid vacation

We are looking for a talented researcher to drive the development and application of LLMs in the world of quantitative finance. In this role, you will harness your expertise in deep neural networks to architect, fine-tune, and push the frontier of these models for financial applications.

• Fine-Tuning on Financial Corpora: Adapt pre-trained LLMs to custom-built financial datasets which predict the market.
• Leveraging Common Crawl Data: Develop robust data pipelines to process and integrate large-scale web data, enriching our models with diverse, real-world information.
• Modifying LLM Architectures: Experiment with adjustments to LLM architectures and training algorithms to enhance performance in financial contexts.

• PhD in Machine Learning, Computer Science, or a related field.
• Extensive hands-on experience in Neural Network development and fine-tuning, using PyTorch, Hugging Face, or similar frameworks.
• Deep understanding of deep-learning architectures and large-scale data processing pipelines.
• Proven ability to translate cutting-edge AI research into real-world applications beyond theory.