Software Engineer, Machine Learning at Lyft
San Francisco, California, United States
🇺🇸 (Posted Oct 31 2018)
About the company
Lyft was founded in June 2012 by Logan Green and John Zimmer to improve people’s lives with the world’s best transportation. Lyft is the fastest growing rideshare company in the U.S. and is available to 95 percent of the US population as well as in Ontario, Canada. Lyft is preferred by drivers and passengers for its respectful and friendly experience, and its commitment to effecting positive change for the future of our cities.
At Lyft, community is what we are and it’s what we do. It’s what makes us different. To create the best ride for all, we start in our own community by creating an open, inclusive, and diverse organization where all team members are recognized for what they bring.
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 Marketplace, Mapping, Fraud, Growth and beyond. 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. Our highly motivated Machine Learning Engineers work on these challenging problems and define solutions to directly impact various aspects of our core business.
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.
Design, build, train and test Machine Learning models
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
Skills & requirements
B.S., M.S. or Ph.D. in Computer Science or related technical field
5+ years (or Ph.D. with 2+ years) of industry or research experience developing ML models
Deep knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, etc
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
“Engineer at heart” with a high degree of comfort in designing software systems and producing high-quality code
Strong oral and written interpersonal skills