Build ML and optimization models that set real-time prices and ETAs at Lyft scale

Lyft is hiring an Applied Scientist for its Dynamic Pricing & Offer Selection team, sitting at the core of the Pricing org. You'll build ML and operations-research models that determine real-time prices and ETAs, productionize pipelines handling millions of calls per day, and balance supply and demand across Lyft's two-sided marketplace. This is a hybrid role in San Francisco (in-office Mon/Wed/Thu).

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What this interview tests

ML and operations-research model design for real-time pricing/ETA decisions
Productionizing models/pipelines at scale (millions of calls/day)
Framing ambiguous marketplace problems mathematically
Balancing short-term conversion vs. long-term retention in a two-sided marketplace
Evaluating ML systems against business goals, not just offline metrics
Cross-functional collaboration with PMs, engineers, and analysts

Common question themes

Tell me about an ML or optimization model you built and productionized end to end.

How do you frame a real-world marketplace problem (like pricing or ETA) as a mathematical model?

Describe a time an off-the-shelf library or approach didn't fit and you built a custom method.

How do you evaluate a pricing/ML model against business goals rather than just accuracy?

Walk me through balancing short-term conversion against long-term retention in a pricing decision.

How have you collaborated with engineers to get a model live and monitored in production?

How candidates describe it

Real Applied Scientist interview stories — retold from candidates' public write-ups, with sources.

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