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 Rider, Marketplace, Growth and beyond. While traditional approaches to optimization and problem decomposition are sufficient to disrupt transportation, building a 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.

The Recommendations team is responsible for the intelligence behind the content and the offerings that we show to our users in the rider app. This team leverages machine learning to determine what content is most relevant to users, and which content will lead to the highest likelihood of conversion and business value from our users. You will work closely with our rider experience teams to ensure that we are showing the most contextual, relevant, and highest impact content to our users to enable them to make the best decisions possible.

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 to push state of art forward
• Write production-level code to convert your ML models into working pipelines
• Analyze experimental and observational data, communicate findings, and facilitate launch decisions
• Participate in code reviews to ensure code quality and distribute knowledge
• Critically evaluate problems across business areas
• Work closely with cross-functional team to creatively design solution for business impact

• B.S., M.S. or Ph.D. in Computer Science or related technical field or relevant work experience
• 8+ years (or Ph.D. with 6+ 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

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• Access to a Health Care Savings Account
• In addition to provincial observed holidays, team members get 15 days paid time off, with an additional day for each year of service 
• 4 Floating Holidays each calendar year prorated based off of date of hire
• 10 paid sick days per year regardless of province
• 18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible

Lyft proudly pursues and hires a diverse workforce. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy.  Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind.  Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process.  Please contact your recruiter now if you wish to make such a request.

This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Thursdays and a team-specific third day. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year.