Machine Learning Engineer, Lyft Business & Ads

Urban Ridge Supplies; Toronto; Hybrid; CAD 119,000 - 149,000; Full time

Benefits offered by this job

Extended health and dental

Life insurance

Mental health benefits

Family building benefits

Child care and pet benefits

Lyft Health Savings Account

RRSP company match

Flexible PTO

Paid parental leave

Commuter benefits

Job summary

Lyft is seeking a Machine Learning Engineer for Lyft Business in Toronto. You will design, build, and deploy ML systems across pricing, fraud detection, and AI-powered analytics. This role offers hybrid in-office work with several days on-site weekly and opportunities to impact multiple business verticals.

You will collaborate with Data Scientists and Software Engineers to translate complex problems into scalable ML solutions, driving value across rides, healthcare, and enterprise customers.

Qualifications

• Experience with GenAI / LLM ecosystems such as prompt engineering or agents.
• Exposure to graph-based ML methods and knowledge graphs.
• Experience with pricing, marketplace, or fraud-related ML problems.
• Familiarity with cloud ML services (AWS SageMaker/Bedrock).
• Proven ability to define and scope ML projects independently.

Responsibilities

• Develop and deploy ML models across multiple problem domains in production.
• Build agentic AI systems to automate decision-making and reduce overhead.
• Design feature pipelines, training workflows, and serving infrastructure.
• Collaborate with Data Scientists to take research to production at scale.
• Evaluate system performance against business KPIs and iterate.
• Identify opportunities to apply ML across Lyft Business verticals.

Skills

GenAI/LLM ecosystems

Graph-based ML methods

Pricing/marketplace/fraud ML

Cloud ML services

Independent project scoping

Job description

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.