Machine Learning Engineer
Are you interested in the kind of technology that powers Google's Knowledge Graph, IBM Watson, and Wolfram Alpha? Our vision is to build intelligent systems that drive the next generation of enterprise software, and we're not gonna pull it off on our own.
We're looking for someone with experience in natural language processing, information extraction, KR&R, adaptive learning, or web-scale clustering, to help us improve our core inference engines:
- You keep a copy of CLRS or Skiena on your nightstand.
- You follow NIPS/SIGKDD/AAAI/etc like people follow HIMYM or XKCD.
- Nothing is quite like the satisfaction of a higher F-score.
- For halloween you went as Jeff Dean, Andrew Ng, or Oren Etzioni.
- You've had at least one bad dream that started with mvn package and ended with [INFO] BUILD FAILURE
- Any chump can run a parser. You dug into the Stanford/OpenNLP/Berkeley source to understand how they combined recursive neural networks with their probabilistic context free grammar.
Reasons to work with us:
- We're for real: we're venture-backed, we pay well, offer solid equity and full benefits, and will provide you with whatever system/snacks/tools you need to be productive.
- We're seed-stage, so everyone plays an enormous role in driving the company and the product. Even Marco.
- Our offices have beautiful views of the Charles and are stocked with fresh fruit, gourmet soft drinks on tap, unlimited healthy snacks, a fun team, and a hammock.
Kemvi is a venture-backed, seed-stage company in Cambridge, MA. Our customers work in financial services, sales, and pharma.
We build technology that helps them prioritize and process the relentless stream of information that demands their attention. Our vision is to build an intelligent agent that reads the web to learn from the world's collective intelligence, and fuses it with companies' internal data to help them make more effective decisions.
We love people who set their own objectives, who can both make decisions and bring them to life, and who care deeply about their work.