Senior Research Scientist

Kensho is a 120-person Machine Learning (ML) and Natural Language Processing (NLP) company, centered around providing cutting‑edge solutions to meet the challenges of some of the largest and most successful businesses and institutions. We are owned by S&P Global and operate independently. Our toolkit illuminates insights by helping the world better understand, process, and leverage messy data. Specifically, Kensho’s solutions largely involve speech recognition (ASR), entity linking, structured document extraction, automated database linking, text classification, and more.

We are continuously expanding our portfolio and are looking for passionate researchers to help us create state‑of‑the‑art models across a variety of domains! Are you looking to solve hard problems and enjoy working with teammates with diverse perspectives? If so, we would love to help you excel here  are a collaborative group of experienced Research Scientists and Machine Learning Engineers, whose academic backgrounds include doctorate degrees in NLP, theoretical physics, statistics, etc.

We take pride in our team‑based, tightly‑knit startup‑like Kenshin community, which fosters continuous learning and a communicative environment.

Since 2022, we have been building a world‑class R&D lab comprised of NLP Research Scientists, and we heavily prioritize publishing in top‑tier conferences. Our small team has demonstrated compelling results and is fueling innovation throughout Kensho and S&P Global cifically, we are continuously developing Large Language Models (LLMs) and are actively working on long‑context question‑answering (QA), complex reasoning, tokenization, alignment (e.g., factuality), multi‑document QA, and more!

Our small team has reserved access to hundreds of fast GPUs (A100s), spanning Cloud and on‑prem machines.

• Long‑context document QA, where the answer is contained within documents that are hundreds of pages in length [1]
• Complex reasoning, including better understanding and improving models’ ability to approximate numbers (related to commonsense reasoning).
• Creating rigorous evaluation benchmarks, spanning domain knowledge, quantity extraction, and program synthesis [2]
• Improving existing alignment techniques for domain‑specific needs, while also addressing factuality
• Dissecting tokenizers to better understand how each of the sub‑components impact intrinsic and extrinsic performance [3][4]
• Multi‑Document QA where the answer requires combining information from dozens of sources.
• Retrieval‑augmented generation (RAG) methods
• Creating high‑quality data filters for LLM development

• Regularly reading late‑breaking research papers and helping to identify the most promising problems to pursue
• Serving a leading role on a research project
• Developing novel, state‑of‑the‑art NLP models that can scale to millions of documents
• Working closely with other Research Scientists and ML Engineers
• Writing clean, readable research code in PyTorch (not expected to write production‑level code)
• Contribute to a stellar engineering culture that values excellent design, documentation, testing, and code
• Share your research results with your colleagues (presentations) and the world (published papers, patents, and blog posts)

• Hold a PhD in Computer Science or related field
• Have several years of post‑PhD research experience in industry or academia
• Have a strong publication record with top‑tier ML/NLP conferences (e.g., ACL, NAACL, EMNLP, NeurIPS, ICML)
• Are proficient in writing code in PyTorch, Tensorflow, or JAX
• Experience with leading research projects with others (e.g., last‑author papers), including directing the vision and providing regular feedback
• Have experience with the techniques required to work effectively with large, messy real‑world data
• Prefer to collaborate iteratively on hard problems with your teammates rather than spending stretches of time working alone and presenting your results intermittently
• Have a love for learning new skills and domains Are excited to share knowledge freely, proactively, and effectively with others who are interested (e.g., participate in our Reading Group)
• Are a…