Machine Learning Engineer
Minsk, Belarus
Lyft
Rideshare with Lyft. Lyft is your friend with a car, whenever you need one. Download the app and get a ride from a friendly driver within minutes.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
- 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
- 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
- Professional and stable working environment.
- The latest technology and equipment you need.
- English classes with native speakers.
- 28 calendar days for vacation and up to 10 paid days off.
- Spacious office facing National Library + opportunity to work remotely
Tags: Computer Science Keras Machine Learning ML models MXNet Pipelines PyTorch Research Scikit-learn TensorFlow
Perks/benefits: Career development Startup environment
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