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ML Software Engineer, Routing
Posted by Lyft on 14 October 2026. Still on their Greenhouse board when we checked 16 min ago. Half of Lyft's roles come down within 22 days.
What the posting is about
Build and maintain Lyft's routing system for millions of daily ride requests. Collaborate with cross-functional teams to improve route efficiency, accuracy, and reliability. Work with a tech stack including AWS, Kubernetes, Go, Python, and Apache Airflow. Enjoy extended health benefits, flexible PTO, and a hybrid work schedule.
Read out of the posting
LevelNot stated
Experience asked3+ years
EmploymentFull time
LocationToronto, Canada
RemoteNot stated
Visa sponsorshipNot stated
SalaryNot published, and most postings do not
Posted2026-10-14
Found viagreenhouse, direct from their system
We had it within the hour it went up.
The posting, as the company wrote it
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.
Our transport network serves the needs of millions of people every day who want to get from one place to another using Lyft cars, bikes and scooters, or on foot in the most efficient way. To serve these needs, we need to suggest the fastest, most affordable and safest routes.
We are hiring a Machine Learning Engineer to join our Routing team. Our team builds and maintains Lyft's system responsible for providing routes for every ride request on our platform. We process millions of route requests per day and low latency, high reliability and high accuracy are paramount for our success.
If you are a critical thinker with experience in machine learning workflows and writing reliable code, passionate about solving business problems using data and working in a dynamic, creative, and collaborative environment, we are searching for you.
Our technology stack runs on AWS, Kubernetes, Go, Python/C++, Apache Airflow, Flink, and Kafka. We use AI to improve development productivity, optimize customer support workflows, and enhance operational efficiency — all without compromising code quality or security. In this role, you will work with incredibly passionate and talented colleagues from software engineering, machine learning and data science on building rideshare experiences that delight millions of riders and drivers.
Responsibilities:
Perform data analysis and build proof-of-concept to explore and compare ML and non-ML solutions
Be able to make effective tradeoffs between model accuracy, its productization complexity and runtime performance
Develop statistical, machine learning, or optimization models
Write production quality code that can scale well to serve millions of requests per day
Participate in code reviews, design reviews, production on-call support and incident triaging process.
Write well-crafted, well-tested, readable, maintainable code
Experience:
B.S., M.S., or Ph.D. in Computer Science or other quantitative fields or related work experience
3+ years of Machine Learning experience
Nice-to-have: Experience with big data processing / distributed data pipelines and tools such as Apache Airflow and Spark/Flink
Proficiency in at least one of the SQL languages (MySQL, PostgreSQL, SqlServer, Oracle)
Ability to work in distributed teams spread across time zones. (EST and PST)
Benefits:
Extended health and dental coverage options, along with life insurance and disability benefits
Mental health benefits
Family building benefits
Child care and pet benefits
Access to a Lyft funded Health Care Savings Account
RRSP plan with company match to help save for your future
In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service
Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
Subsidized commuter benefits and Lyft ride credits
Lyft is committed to creating an inclusive workforce that fosters belonging. 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 if you wish to make such a request.
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 at least 3 days per week, including 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 Toronto area is CAD $118,800 - CAD $148,500, 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.
Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.
This job fills an existing vacancy.
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