Lyft Senior Machine Learning Engineer at Lyft to design, develop, and deploy state-of-the-art machine learning and AI systems. Lead model development, system design, and innovation with a focus on LLMs and real-time applications.
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 learning and collaboration.
Code Quality: Write production-level code and participate in code reviews to ensure quality and share knowledge across the team.
M.S. or Ph.D. in Computer Science or related technical field
5+ years (or Ph.D. with 3+ years) of experience in machine learning modelling or related fields
Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks
Understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learning
Experience with translating state-of-the-art ML research into production systems
Qualification
5+ years (or Ph
Required
M.S. or Ph.D. in Computer Science or related technical field
5+ years (or Ph.D. with 3+ years) of experience in machine learning modelling or related fields
Experience with deep learning technologies for recommendation systems
Understanding of statistical concepts such as hypothesis testing and regression analysis
