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.
The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection.
Agentic AI is at the center of how we scale that mission. We fine-tune and align open-source models, build AI-powered support agents, and develop end-to-end AI agents for safety case management, systems that reason over complex, high-stakes cases and drive them to resolution. SCC brings together ML, data, backend, and product engineers alongside data scientists and operations partners to transform these systems.
As a Machine Learning Engineer on the SCC team, you will fine-tune and align models and build AI Agents that power how riders and drivers get help. Your work spans the full loop: post-training open-source models for our domain, composing them into multi-step agents, and building the evaluation that proves they are safe to ship in a customer-facing, safety-critical setting.
- Post-train and adapt open-source LLMs for SCC use cases using SFT, LoRA, and preference-tuning methods (RLHF, RLAIF, RLVR).
- Design and build AI-powered support agents and end-to-end agents for safety case management using LangGraph or equivalent agentic frameworks.
- Own the evaluation data flywheel, offline and online, that defines what "good" looks like and build benchmarks for the team to hill-climb.
- Turn interaction feedback into training data and learning signals, closing the data flywheel that continuously improves the models.
Responsibilities:
- Conduct literature review and build post-training framework and lifecycle. Curate and process human and synthetic data for SFT/LoRA/RLHF/RLAIF/RLVR, and iterate on model quality for real support and safety tasks.
- Develop, evaluate, and productionize AI agents, designing tools, state, and control flow in LangGraph (or equivalent) and taking them through the full agent development lifecycle.
- Build and scale evaluation frameworks, golden sets, rubric-based grading, LLM-as-judge where appropriate, and regression testing.
- Ship models and agents into real-time production, with the monitoring and guardrails needed to operate them safely at millions of interactions a month.
- Apply traditional ML (classification, ranking, gradient-boosted trees) where it's the right tool, and partner with product, ops, and data science to scope problems and define success metrics.
Experience:
- 3+ years of industry experience in applied ML/AI, inclusive of an MS or PhD in Computer Science, Machine Learning, Artificial Intelligence or a related technical field.
- Post-training experience with open-source models. Hands-on familiarity with fine-tuning and preference-tuning paradigms such as SFT, LoRA, RLHF, RLAIF, and RLVR.
- Agentic development experience. Built and shipped agents with LangGraph or equivalent frameworks, and comfort with the full agent development lifecycle.
- Experience with AI/LLM evaluation. Designed metrics and built offline/online evaluation for generative systems.
- Experience deploying ML/AI applications to real-time production use cases.
- Strong programming skills in Python and hands-on experience with PyTorch.
- Preferred:
- Experience applying ML/AI to customer support or trust & safety workflows — agent assist, routing, resolution recommendation, or abuse/safety detection.
- PhD in Computer Science, Machine Learning, Statistics, or a related technical field.
- Publications at top-tier peer-reviewed research venues (e.g., NeurIPS, ICML, ICLR, ACL, CVPR).
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.