Machine Learning Engineer, Safety and Customer Care AI
$118,800–$148,500 year
HybridToronto, Ontario, Canada
Job Summary
Post-train open-source LLMs for safety and customer care use cases using SFT, LoRA, and preference-tuning methods like RLHF and RLAIF. Design and build AI-powered support agents and end-to-end agents for safety case management using LangGraph or equivalent 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. 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 where it's the right tool, and partner with product, ops, and data science to scope problems and define success metrics.
Required Qualifications
- 3+ years of industry experience in applied ML/AI
- 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
- 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
- Hands-on experience with PyTorch
- Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays
Desired Qualifications
- Experience applying ML/AI to customer support or trust & safety workflows
- 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)
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