The Applied AI team is looking for a senior software Engineer to join the Recommendations team. You will build the backend systems that decide what we surface to riders. You will architect the pipelines and services that turn rider context and signals into the right offer at the right moment, driving a measurable impact on rider experience.
Responsibilities:- Establish engineering best practices and patterns; help uplift the team's craft and drive a culture of engineering excellence
- Drive high-impact projects and innovate new solutions to deliver the best user experience
- Produce and drive scalable system design for large, complex features - from idea through execution and launch
- Mentor engineers on the team, providing technical guidance and supporting their growth
- Champion and evangelize the use of AI tools to accelerate engineering productivity across the team, sharing patterns and best practices that raise the bar for how the team builds
- Write well-crafted, well-tested, readable, maintainable code
- Participate in code reviews to ensure code quality and share knowledge across the team
- Participate in the team's on-call rotation; identify, triage, debug, and resolve issues across our applications and platforms
- Excellent communication skills and the ability to explain the various trade offs made in decisions
- Manage project priorities, deadlines, and deliverables.
Experience:- BS/MS or equivalent in Computer Engineering, Computer Science, or related field or equivalent practical experience.
- 5+ years of software engineering/production infrastructure industry experience.
- Experience with Python and Go.
- Experience with cloud platforms (AWS, GCP, or Azure), distributed systems, and databases such as PostgreSQL or DynamoDB
- Proficient and effective in using AI tools (e.g. Copilot, Claude Code, Cursor) to accelerate coding and engineering workflows
- Proficiency in object-oriented programming.
- Experience working with data structures or algorithms.
- Ability to work with a low-ego, highly collaborative, and cross-functional team.
- Bonus points: Experience building real-time serving systems for ML models, working with recommendation or ranking systems, feature stores, or online/offline experimentation frameworks.
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 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
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 $136,000 - CAD $170,000, 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.
