ML Software Engineer, Routing

Lyft
Toronto, Canada
On-site

Who this role is best for

Strong fit for candidates who build scalable machine learning systems and work in hybrid office environments, with a focus on routing optimization and collaborative tech stacks.

Best fit for

  • Individuals who thrive in hybrid work arrangements with in-office requirements on specific days
    — “work in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week”
  • Professionals with a background in distributed systems and data engineering tools like Airflow and Kafka
    — “Experience with big data processing / distributed data pipelines and tools such as Apache Airflow and Spark/Flink”

Things to consider

  • Hybrid schedule requires in-office presence at least three days per week, including specific days
    — “work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays”
  • The role does not specify seniority, but likely expects strong ML and software engineering experience
    — “3+ years of Machine Learning experience”

How to stand out

  • Highlight experience in optimizing models for low-latency, high-throughput environments
    — “process millions of route requests per day and low latency, high reliability and high accuracy are paramount”
  • Showcase a history of writing production-quality, maintainable code for large-scale systems
    — “Write production quality code that can scale well to serve millions of requests per day”
  • Emphasize collaboration with cross-functional teams in ML and software engineering
    — “work with incredibly passionate and talented colleagues from software engineering, machine learning and data science”
  • Demonstrate familiarity with AWS, Kubernetes, and distributed data tools like Kafka and Flink
    — “Our technology stack runs on AWS, Kubernetes, Go, Python/C++, Apache Airflow, Flink, and Kafka”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • successful model deployment
  • low latency and high reliability systems
Typical background
machine learning experiencesoftware engineering background

Skills & requirements

Required

Machine LearningData AnalysisModel DevelopmentProduction Quality CodeCode Reviews

Preferred

Big Data ProcessingDistributed Data PipelinesAI In Development Productivity

Stack & domain

Machine LearningData AnalysisProof-of-conceptStatistical ModelsOptimization ModelsProduction Quality CodeLow LatencyHigh ReliabilityHigh AccuracyAWSKubernetesGoPython/c++Apache AirflowFlinkKafkaAIDevelopment ProductivityCustomer Support WorkflowsOperational EfficiencyCode QualitySecuritySQLBig Data ProcessingDistributed Data PipelinesSpark/flinkSQL LanguagesMySQLPostgreSQLSqlserverOracleDistributed TeamsESTPSTCritical ThinkingProblem-solvingCommunicationTeamworkAdaptabilityTechnical UnderstandingInnovationDocumentationCross-functional CommunicationAgile MethodologiesFinancial ProductsFixed IncomeCreditEquitiesDerivativesAccountingSettlementAffirmation ProcessingRoutingTransport NetworkOptimizationCloudDevOpsFinance

About the role

Original posting from Lyft

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.

Source: Lyft careers

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