Founding Applied AI Scientist

About the job

Tensorlake is building a distributed data processing platform for developers building Generative AI applications. Our product, Indexify, enables the building of continuously evolving knowledge bases for Large Language Model applications by allowing structured extraction algorithms on unstructured data. We are looking for folks who love to work in the intersection of information retrieval, large-scale distributed systems, and artificial intelligence for content understanding.

 

The Role

As a founding engineer of Tensorlake, you will work closely with the Founder to build the Content Understanding AI algorithms powered by our platform. The scope of the work will span researching new approaches for structured extraction from documents, video and audio understanding algorithms, prototyping, and benchmarking algorithms from cutting-edge research and writing about them.

You will

• Implement cutting edge retrieval algorithms to build Retrieval Augmented Generation applications.
• Evaluate various retrieval algorithms for various use cases and publish results to our users and customers.
• Design and implement cutting-edge deep learning algorithms for extracting unstructured data from video, audio, or raw text.
• Help shape the culture of Applied AI Research in the company.

 

Basic Qualifications

• 3+ years of relevant work experience
• Experience designing and implementing deep learning algorithms for content understanding.
• Ph.D. or Bachelor's degree in Math, Computer Science, or other quantitative fields, OR equivalent experience.

 

Preferred Qualifications

• 5+ years of experience building NLP models/applications and retrieval systems.
• Solid fundamentals in algorithms, data structures, and system design
• Domain expertise in LLMs and generative AI

 

Bonus Qualifications

• Experience working with systems engineering aspects of LLMs (e.g., distributed training, autoscaling inference, etc.)
• Experience in building Knowledge Augmented Language Model applications
• Experience with Retrieval Augmented Generation applications
• Experience with approaches to LLM model improvement and fine-tuning (such as LoRA and RLHF)
• Published research in the Gen AI space

 

Even if you only fit some criteria but are generally excited about this space, we encourage you to apply. Please share a relevant project you have built in the past, and a bonus if it's open source!