Machine Learning/NLP Engineer

Manhattan, New York, United States · Engineering

Description

Alpha (alphahq.com) is a venture-backed start-up based in New York, NY and our platform enables management teams to make data-driven decisions about users, products, and new markets.

At Alpha we are passionate about enabling teams to make data-driven decisions about users, products, and new markets. We believe the best path there involves more shots on goal – by vastly accelerating our clients’ abilities to learn we can help them build better products faster. We’re looking for a talented Machine Learning/NLP Engineer with a strong appreciation for simple, effective architecture and rapid experimentation. This position is full-time and on-site at our SoHo (NYC) office.

Responsibilities:

- Independently work on end-to-end development of NLP models to derive insights from text- Lead NLP projects and develop models in collaboration with team members
- Mentor less experienced members of the team
- Work with stakeholders to refine requirements and communicate progress
- Work with the team to develop a system for semantic search, entity recognition, knowledge graph creation, transcription, paraphrase detection, question answering etc.
- Train deep learning models with internal and external NLP datasets
- Deploy models to production and monitor performance
- Develop original ideas to create cognitive systems
- Participate in internal and external forums

Requirements

- 3+ years of NLP experience
- MS in Computer Science with NLP specialization. PhD preferred

Helpful to have:
- Extensive experience in applying different NLP techniques to problems such as sentence summarization, question answering, sentiment analysis, knowledge extraction and conversational bots
- Expertise in NLP methods such as LSA, LDA, Semantic Hashing, Word2Vec, LSTM, BiDAF etc.
- Strong command over linear algebra and statistics having the ability to quickly translate ideas to efficient, elegant code
- Development experience in Python or Java/Scala with good command over respective data pipelining, matrix algebra and statistics libraries
- Stanford CoreNLP and other NLP tool kits
- Deep learning programming experience with Python/Tensorflow or similar library in a GPU environment
- Experience working with external reference datasets like SQUAD, SemEval, MSRP, WikTable, WikiQA, AllenAI etc.
- Tuning and optimization of sequential deep learning models
- Model deployment and scaling experience

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