Senior ML Software Engineer, Mapping

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.

The Mapping team at Lyft is tasked with building a digital representation of the physical world - a map. We collect and serve the freshest and most accurate mapping data possible, along with algorithms, models, platform services, and map-based user experiences that power Lyft’s current and future transportation offerings.

Mapping represents a huge opportunity for Lyft’s business, but also a big challenge. We build and scale systems that deal with large data storage, real-time data processing, machine / deep learning pipelines, routing and ETA models, driver and passenger location tracking, and more. We built beautiful and magical user experiences on top of all those services, and compete with companies that have been in the mapping business for decades.

To strengthen our efforts, we are hiring a Senior ML Engineer who will work end-to-end on creating and improving new capabilities to detect changes in the environment and reflect them in our Lyft map using a wide variety of input sources from the Lyft fleet. For this we are looking for someone who values software engineering best practices, loves the algorithmic and geospatial side of the challenge and is data-driven from start to end.

Our technology stack ranges from basic machine learning models to large language models and running them at scale on millions of images. You will work with incredibly passionate and talented colleagues from machine learning, data science, and engineering on projects that delight our passengers and drivers – powered by an up to date map.

Responsibilities:

Partner with Engineers, Data Scientists, Product Managers, and Business Partners to apply machine learning for business and user impact

Perform data analysis and build proof-of-concept to explore and propose ML solutions to both new and existing problems

Develop statistical, machine learning, or optimization models

Write production quality code to launch machine learning models at scale

Evaluate machine learning systems against business goal

Experience:

B.S., M.S., or Ph.D. in Computer Science or other quantitative fields or related work experience

5+ years of Machine Learning experience

Passion for building impactful machine learning models leveraging expertise in one or multiple fields.

Proficiency in Python, Golang, or other programming language

Excellent communication skills and fluency in English

Strong understanding of Machine Learning methodologies, including supervised learning, forecasting, recommendation systems, reinforcement learning, and multi-armed bandits

Benefits:

Great medical, dental, and vision insurance options with additional programs available when enrolled

Mental health benefits

Family building benefits

Child care and pet benefits

401(k) plan with company match to help save for your future

In addition to 12 observed holidays, salaried team members have discretionary paid time off, hourly team members have 15 days paid time off

18 weeks of paid parental leave. Biological, adoptive, and foster parents are all eligible

Subsidized commuter benefits

Monthly Lyft credits and complimentary Lyft Pink membership

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. 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, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the San Francisco area is $162,800 - $203,500, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.