Machine Learning Engineer, Safety and Customer Care AI
Builds and productionizes post-trained open-source language models and AI agents for safety and customer-care workflows. The role requires applied ML/AI experience, Python and PyTorch expertise, agentic development, generative AI evaluation, and real-time deployment experience.
About the job
Responsibilities
- Conduct literature reviews and build post-training frameworks and lifecycles.
- Curate and process human and synthetic data for SFT, LoRA, RLHF, RLAIF, and RLVR, iterating on model quality for support and safety tasks.
- Develop, evaluate, and productionize AI agents, designing tools, state, and control flow in LangGraph or equivalent frameworks.
- Take agents through the full agent development lifecycle.
- Build and scale evaluation frameworks, golden sets, rubric-based grading, LLM-as-judge systems where appropriate, and regression testing.
- Ship models and agents into real-time production, with monitoring and guardrails for safe operation at millions of interactions per month.
- Apply traditional machine learning, including classification, ranking, and gradient-boosted trees, where appropriate.
- Partner with product, operations, and data science teams to scope problems and define success metrics.
Requirements
- 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.
- Experience building and shipping agents with LangGraph or equivalent frameworks.
- Experience with the full agent development lifecycle.
- Experience designing metrics and building offline and online evaluations for generative systems.
- Experience deploying ML/AI applications to real-time production use cases.
- Strong programming skills in Python.
- Hands-on experience with PyTorch.
Nice-to-haves
- Experience applying ML/AI to customer support or trust and safety workflows, including agent assist, routing, resolution recommendation, or abuse and safety detection.
- PhD in Computer Science, Machine Learning, Statistics, or a related technical field.
- Publications at top-tier peer-reviewed research venues such as NeurIPS, ICML, ICLR, ACL, or CVPR.
Compensation and Benefits
- Expected base pay range in the Toronto area: CAD $118,800–$148,500, excluding potential equity, bonus, and benefits.
- Extended health and dental coverage, life insurance, and disability benefits.
- Mental health benefits.
- Family-building benefits.
- Child care and pet benefits.
- Lyft-funded Health Care Savings Account.
- RRSP plan with company match.
- Flexible paid time off for salaried team members; hourly team members receive 15 days paid time off, with an additional day for each year of service.
- 18 weeks of paid parental leave for eligible biological, adoptive, and foster parents.
- Subsidized commuter benefits and Lyft ride credits.
Skills
Python, PyTorch, LangGraph, LLMs, Sft, Lora, RLHF, Rlaif, Rlvr, Model Fine-Tuning, AI Agents, Machine Learning Evaluation, Real-Time Production, Gradient-Boosted Trees
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