Lyft · Toronto
Machine Learning Engineer, Lyft Business & Ads
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...
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.
Machine Learning is at the heart of Lyft’s products and decision-making. Machine Learning Engineers at Lyft operate in dynamic
environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges, from
pricing and marketplace frameworks that ensure reliability and competitiveness, to agentic AI platforms that automate analytical
workflows, to behavioral detection systems that protect the integrity of our network. We operate at the intersection of applied ML
and real business impact, shipping models that directly influence revenue, rider experience, and partner trust.
Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and
guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare
rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated
tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and
community programs.
We're looking for a Machine Learning Engineer to design, build, and deploy ML systems across Lyft Business. This is a high-scope
role: you won't be siloed into one problem area. Instead, you'll move across pricing algorithms, fraud and behavior detection,
agentic AI systems, and emerging ML applications as the business evolves. You'll write production-quality code, own models
end-to-end from prototyping through deployment, and collaborate closely with Data Scientists, Product Managers, and Software
Engineers to translate complex business problems into scalable ML solutions.
This role is ideal for someone who is technically versatile, energized by variety, and wants to see their work directly shape a
large-scale business.
RESPONSIBILITIES:
- Develop and deploy ML models across multiple problem domains — including dynamic pricing, marketplace optimization, fraud
detection, and anomaly/behavior detection — in production environments serving millions of rides
- Build and iterate on agentic AI systems (e.g., LLM-powered analytical agents) that automate decision-making and reduce
operational overhead
- Design and implement feature pipelines, model training workflows, and serving infrastructure using Lyft's ML platform
- Partner with Data Scientists on the Algorithms and Decisions teams to take research prototypes from proof-of-concept to
production at scale
- Evaluate ML system performance against business KPIs, run experiments, and drive continuous model improvement
- Identify new opportunities where ML can create leverage across Lyft Business verticals (Healthcare, Lyft Pass, Business Travel)
and pitch solutions
- Contribute to team engineering standards — code quality, observability, documentation, and testing practices
EXPERIENCE:
- Experience with GenAI / LLM ecosystems — prompt engineering, RAG, agent frameworks (e.g., LangChain, LangGraph), or fine-tuning
- Exposure to graph-based ML methods (graph neural networks, knowledge graphs, network analysis)
- Experience with pricing, marketplace, or fraud-related ML problems
- Familiarity with cloud ML services (AWS SageMaker, Bedrock) or internal ML platforms
- Track record of identifying and scoping ML projects independently, not just executing on pre-defined specs
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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Machine Learning Engineer, Lyft Business & Ads
Kategori
Central MLB
Plats
Publicerad
9 juli
Sista ansökningsdag
17 augusti
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