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greenhouse

ML Software Engineer, Routing

Lyft
Toronto, CAN
Mid · 3+ years experience
Onsite
Discovered 1 weeks ago
airflowflinkmysqlpostgresqlsparksql

Full Job Posting

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.

Preferred Qualifications

  • Nice-to-have: Experience with big data processing / distributed data pipelines and tools such as Apache Airflow and Spark/Flink

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