LLM Performance Researcher

As an LLM Performance Researcher at Endeavor, your primary responsibility will involve designing and conducting experiments to enhance the extraction, normalization, and matching of various real-world documents. You'll also critically evaluate the performance of LLMs on challenging inputs like scanned PDFs and OCR outputs. Another key duty is to improve model accuracy, specifically dealing with rare document formats and domain-specific vocabulary, ultimately contributing to the efficiency of our agentic ERP.

To excel as an LLM Performance Researcher at Endeavor, candidates typically need a PhD or an equivalent research background in NLP, ML, or a related field. Nonetheless, hands-on experience is valued above formal qualifications, so if you have substantial experience in document understanding and information extraction using LLMs, you are encouraged to apply.

At Endeavor, we pride ourselves on a fast-paced environment where research is directly applied to production code. As an LLM Performance Researcher, you will collaborate with Applied AI Researchers and Backend Engineers to rapidly deploy improvements you've discovered through your experiments, which emphasizes our commitment to bridging the gap between theoretical studies and real-time applications.

The work at Endeavor is dynamic and collaborative, embracing a culture where innovation thrives. As an LLM Performance Researcher, you will operate in a setting where your ideas are honored and valued, and your input can significantly impact project directions. Additionally, our focus on the practical implementation of research findings ensures that you'll see your contributions come to life.

Endeavor is committed to continuous growth for our LLM Performance Researchers. You'll have numerous opportunities for professional development, whether it's through participating in cutting-edge projects, collaborating with experienced peers, or accessing ongoing training resources. We encourage innovation and exploration, allowing you to expand your skills and knowledge while tackling exciting challenges.

In answering this question, highlight specific projects where you successfully utilized LLMs for document understanding. Discuss how you approached challenges related to extraction, normalization, or entity linking. Demonstrating a clear understanding of both academic benchmarks and real-world applications will set you apart.

When responding, outline a strategy that encompasses systematic testing of various noisy inputs, including using benchmarks and allowing for real-world failure modes. Show your knowledge of specific tools and methodologies that have been effective in your past projects, reinforcing your experience.

Your answer should focus on techniques you employ to enhance model accuracy, such as fine-tuning LLMs to better handle domain-specific terminology or utilizing augmentations for noisy data. Mention any experience with relevant tools or frameworks that assist in refining accuracy.

Share how you regularly engage with academic literature, attend conferences, or collaborate with peers in the field. Emphasize your enthusiasm for continuous learning and how you apply new insights to your projects at Endeavor.

Provide a specific example from your past experience, detailing the research you conducted, the findings obtained, and how these influenced your team’s production efforts. Highlight any measurable impacts this had on performance or efficiency.

Real-world challenges may include dealing with model failures or unexpected behaviors. Discuss specific instances, focusing on your problem-solving process and the resolutions you developed. This shows your capability in navigating typical hurdles in the field.

Your response should outline the systematic approach you take to schema alignment, including the tools and frameworks used. Discuss how you ensure accuracy and efficiency in transferring data between aligned schemas.

Discuss the importance of evaluation quality and reproducibility in your work, and detail any protocols you follow to ensure consistency in your findings. Mention tools or practices employed to maintain high evaluation standards.

Explain how you effectively communicate complex ideas with team members and stakeholders. Share examples of successful collaborations and how teamwork contributed to achieving research goals, emphasizing the importance of diverse perspectives.

This is an opportunity to express your vision and insights on potential developments in LLMs, particularly in enterprise settings. Show your awareness of current trends and how they could evolve, and convey how you’d like to contribute to that future at Endeavor.