Founding ML Engineer, Trust & Safety

At Lyft, our purpose is to serve and connect. To do this, we start with our own community by creating an open, inclusive, and diverse organization.

The Community Safety organization is responsible for ensuring the trust & safety of our community of riders and drivers. We work every day toward building a trusted community, fostering safe interactions, managing unsafe situations, and providing best-in-class support. As a member of our Community Safety team, you will play a critical role in making transportation safer for all.

Leading machine learning (ML) efforts on the Community Safety team you will work in a dynamic environment, where we embrace moving quickly in efforts to build the world’s safest transportation. In particular, you’ll work to prevent and reduce community safety risks through our products upstream — building 0-1 generative artificial intelligence (AI) models, and upleveling our machine learning models that mitigate risk as well. Join us and take on some of the most interesting and impactful problems in ridesharing.

Responsibilities:

• Develop and deploy models in support of community trust & safety and lead continuous monitoring, evaluation against goals, and improvement.
• Collaborate with cross-functional teams including data scientists, other machine learning engineers, and both technical and non-technical stakeholders.
• Perform data analysis and build proof-of-concept to explore and propose machine learning solutions to both new and existing community safety problems.
• Write production quality code to launch machine learning models 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.
• Explore, develop, and deliver cutting-edge technologies for trust and safety systems.
• Set up and conduct large-scale experiments to test hypotheses and drive product development.

Experience:

• The ideal candidate will have expertise in both classical ML algorithms as well as deep learning algorithms, and additionally possess hands-on experience developing complex ML systems.
• B.S., M.S., or Ph.D. in Computer Science or other quantitative fields or related work experience.
• 5+ years of ML experience.
• Passion for building impactful ML models leveraging expertise in one or multiple fields.
• Proficiency in Python. (Golang, or other programming language are nice-to-have but not required.)
• Experience with CatBoost/XGBoost. (LLMS experience is nice-to-have but not required.)
• Strong understanding of ML and software lifecycle management and associated tools (ETL pipelines, CI/CD, feature stores, inference frameworks).
• Robust statistical modeling skills (hypothesis testing, causal inference).
• Passion for staying current with the latest developments in data science and ML.
• Curious mindset, self-starter attitude, and eagerness to learn and develop professionally.
• Excellent communication skills and fluency in English.
• Excellent feature engineering and signal generation skills.
• Strong understanding of supervised and unsupervised learning approaches with different data modalities in domains including computer vision and fraud detection.

Benefits:

• Extended health and dental coverage options, along with life insurance and disability benefits
• Mental health benefits
• Family building benefits
• 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 proudly pursues and hires a diverse workforce. 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 now if you wish to make such a request.

This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. 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 $149,600 - CAD $205,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.