Position: Machine Learning Engineer  (NLP)
Company: Lyft
Experience required: 3y
Category: Data Science
English level: upper
Published: 2021-09-01T00:00:00+03:00

At Lyft, our mission is to improve people’s lives with the world’s best transportation. To do this, we start with our own community by creating an open, inclusive, and diverse organization.

With over half a billion rides and counting, Lyft is solving hard problems in a rapidly growing domain with a lot of data and creative solutions in multiple domains, including Mapping and Search. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building next-generation platform for low-cost, ultra-immersive transportation to improve people’s lives warrants modern ML utilizing peta-byte scale data. We are building an in-house search engine to help our riders and drivers find the right spots and places to efficiently get to their destinations. 

If you are a critical thinker with experience in machine learning workflows, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.

**Responsibilities:**
— Design, build, train, evaluate and test Machine Learning models, focusing on Search applications
— Write production-level code to convert your ML models into working pipelines
— Work closely with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context
— Analyze experimental and observational data, communicate findings, and facilitate launch decisions
— Participate in code reviews to ensure code quality and distribute knowledge

 **Experience:**
— B.S., M.S. or Ph.D. in Computer Science, related technical field or relevant work experience
— 3+ years of industry or research experience developing ML models
— Experience in modern Deep Learning and Natural Language Processing (NLP) techniques and frameworks, including Transformers, seq2seq with attention, RNNs, gensim, fasttext, spacy, scikitlearn, pandas;
— Proven ability to quickly and effectively turn research ML papers into working code
— Practical knowledge of how to build efficient end-to-end ML workflows