Senior Machine Learning Engineer, Recommendations

Excerpt from the original posting

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

With a billion rides per year 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.

If you are a critical thinker with experience in machine learning workflows and LLMs, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.

We are seeking a Senior Machine Learning Engineer to join the Rider Applied AI team and lead the design, development, and deployment of state-of-the-art machine learning and artificial intelligence systems. This role requires a strategic thinker who can balance high-level system architecture with hands-on technical implementation. You will collaborate across teams to shape the future of ride-sharing by leveraging AI, Machine learning and Data science.

Responsibilities: 

- Model Development & Research:  Design, build, and deploy machine learning models for real-time applications, including translating state-of-the-art research into production-ready solutions.

- System Design:  Architect scalable, reliable ML pipelines that integrate seamlessly with existing backend systems.

- Innovation & Applied Research:  Stay ahead of the curve by exploring emerging algorithms, technologies (such as LLMs and LLM-based applications), and frameworks — critically evaluating new research and identifying high-impact use cases across business areas.

- Collaboration:  Partner with ML engineers, product managers, data scientists, and software engineers to align ML initiatives with business goals.

- Data-Driven Decision Making:  Leverage data-driven insights to inform and refine ML strategies and solutions.

- Mentorship & Technical Leadership:  Provide technical direction, mentor Junior engineers, and foster a culture of learni…

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