Sr. Machine Learning Engineer

If you’re excited about:

• Using state of the art Learning techniques and applying to Search

• Working end to end on Machine Learning Models lifecycle

• Do all-around work: architecture, engineering, quality and business

• Shaping the course of a fast-growing company and product category

• Working with people who value clarity, creativity, passion, initiative, and thoughtfulness.

• Learning from world class ML/AI researchers and engineering leaders

Then this is your opportunity to join an early-stage, fast-growing, and well-funded startup to shape the course of an entire industry.

Pinecone is pioneering search and database technology that's revolutionizing how developers build search systems. Our managed vector database provides customers with easy-to-use capabilities that until now have only been in the hands of a few tech giants. The demand for vector databases is growing incredibly fast, and it shows in our user base. We have a large audience of ML engineers, software engineers, data scientists, and technical architects who are eager to learn and build with our product.

As a Machine Learning Engineer at Pinecone’s Relevance team, you will work on improving the Pinecone vector database search functions. You will use data and machine learning in order to build embedding pipelines and re-ranking models. You will engineer systems end to end, and work closely with world class researchers. As a leading member of the Relevance team, you will have direct influence on our product and the ability of companies of all sizes to use it effectively.

Responsibilities: 

• Architecting and engineering embedding models
• Architecting and engineering end to end data collection to models systems
• Working closely with Pinecone’s Science team on learning to retrieve

What we look for: 

• Experience in MLOps and Machine Learning infrastructure
• Proficiency in Python + one of Java/Scala/Go/Rust/C++
• Experience in model training, deployment, CICD, cloud environments
• Experience in Microservices, distributed systems, serving models in production environment, Big Data Systems

Bonus points for experience with:

• Semantic Search and NLP
• Architecture of large scale production systems
• Lucene (Elasticsearch) or other lexical search systems 
• Data Lakes: Data processing infrastructure, Data cataloging, Data Quality.