Position: Frontend Engineer
Location: Brooklyn, NY (commutable from NY or Long Island ? against traffic)
? Verticals: Financial Services, Machine Learning, Health Care Information Technology, Natural Language Processing
? Equity, bonuses, medical / dental, ping pong
? Front-end projects:
? Custom data visualizations: improve data insights in order to enable the discovery and visualization of interesting business trends. Optimize data visualizations computed over big data.
? Document converter: develop in-house tools for converting PDF, Word, Excel etc. into formats amenable for machine learning and user annotation.
? User notification system: improve the existing platform-wide notification system in order to deliver significant user engagement uplift.
? Cross-browser compatibility.
UI-centric AI platform ISO Frontend master
What you get:
? Be part of a team that is bringing deep NLP to Internet scale
? Make novel contributions to how computers understand text
? Work with top researchers in NLP and ML (MIT PhDs)
About you: You will engineer sleek solutions for visualization, presentation, and interaction with high volumes of semantically-rich longitudinal business intelligence data, resulting in beautiful and easy to use web applications that render a consistent output. You have experience developing in OSX and/or Linux. You?ve had positive experience working for a startup before.
Your users: non-technical enterprise users, in both desktop and mobile settings. Educate us on the possibilities.
? Working knowledge of ReactJS
? Passionate about UX and the challenge of designing elegant UIs for non-tech-savvy users
? Creative with both code & markup
? Bonus: iOS, Spring
About us: We operate an infrastructure-as-a-service for deep NLP. Our non-technical clients can use our front-end UI to interactively train and deploy natural language pipelines that meet their specific
information needs. Developers can use our API to build language understanding apps, by accessing a deep NLP pipeline that processes billions of Internet posts as well as internal text data.